title: Identifying barriers and access to equitable allocations of federal disaster assistance
title: the influential roles of social vulnerability and social capital and their interaction
titleType: Subtitle
Creators: name: De La Torre, Cristina Munoz
Publisher: name: University of Iowa
Year: 2027
Subjects: disaster recovery
distributive justice
equity
federal resources
social capital
social vulnerability
public abstract

description: Climate disasters are increasing in frequency and severity and repeatedly impacting local communities beyond their capacity to respond and recover. This is why requests and approvals for presidentially declared disasters are on the rise. Federal agencies responding to local disaster relief are operating under constrained funding and seeking to effectively and equitably allocate disaster assistance. Understanding barriers and access to federal disaster assistance is a key step toward an equitable federal disaster response and recovery.
description: This dissertation implements social vulnerability and social capital theory to develop a conceptual framework to understand barriers and access to federal disaster assistance and implements an equity analysis to identify the extent to which allocations from various federal disaster assistance programs are equitable. This conceptual framework is implemented across three studies. The first study models the relationship between social vulnerability and federal disaster assistance through an Iowa study area affected by floods in 2008. The second study tests the influence of pre-disaster and post-disaster social capital to federal disaster assistance through a Texas and Louisiana study area affected by the 2016 floods. The third study investigates the interaction between social vulnerability and social capital and the extent to which this interaction influences allocations of federal disaster assistance in a national and panel case study.
description: Across the three studies, there are three main findings. First, who is most socially vulnerable and least served by federal disaster assistance is highly context-specific by federal program, disaster, scale, and geography. However, renters, low-income, and communities of color were more likely to be underserved across different contexts. Second, bonding social capital had a positive influence on allocations of federal disaster assistance for individuals and households but a negative influence on allocations for state, county, and other localities. On the other hand, bridging social capital had the opposite effect of bonding social capital. Thus, different types of social capital are more influential at different scales of federal disaster assistance implementation and allocation. Third, there is an interaction effect between social vulnerability and social capital in which bridging social capital, although generally increases access to federal Public Assistance, did so less for communities with higher social vulnerability. This finding identifies potential inequities within social capital and how it might contribute to inequities in the allocations of federal disaster assistance.
description: As a whole, this dissertation addresses two significant knowledge gaps. The field of study of social vulnerability in federal disaster assistance is relatively new but quickly growing. It is an important study area due to its relevancy in policy implications. However, there remains a significant knowledge gap in the role of social capital to federal disaster assistance, although many scholars and practitioners have identified social capital as an important component of disaster resilience. Finally, there is a general knowledge gap in the field of disaster studies around the interaction of social vulnerability and social capital and their influence on various disaster outcomes, including federal disaster assistance. This dissertation contributes to this knowledge gap through the development of a conceptual model including social capital theory, implementation of an equity analysis, and the application of robust geostatistical modeling.