RAPID: Constraints on Policy Learning After Disaster

Wayne State University, Detroit MI

Investigators

Abstract

The objective of this study is to understand why communities that have experienced a natural disaster decide to reduce the risk of a future disaster. Communities learn from the experience of disaster but often that learning does not lead to a policy change that reduces risks. Furthermore, communities vary in their capacity to mitigate future disasters. The broader impact of this study is to identify the limits on policy learning about disaster mitigation after a community has experienced a disaster. It is critical to understand why one community may be more vulnerable to a disaster than its neighbor. Hurricane Harvey presents a case for understanding the limits on policy learning after a disaster because of the range of communities it affected across Southeast Texas. Data will be collected using a series of semi-structured interviews over several months with local government officials - mayors, emergency managers and city planners - in communities affected by Hurricane Harvey. The intellectual merit of this project is the use of data collected directly from local government officials on policy learning after a disaster across time. This project asks what are the constraints on policy learning after disaster? The central theory of this project is that local governments will attempt to engage in policy learning after a natural disaster but those efforts will not necessarily lead to a policy change that reduces future risk. This theory is based on the idea that the process of policy learning can stall or halt when local governments are assessing information about mitigating natural hazards. This study examines policy learning after disaster in the context of Hurricane Harvey because of the range of communities it impacted across Southeast Texas. It hypothesizes that information is the critical factor for policy learning and that learning can be constrained by (1) the types of sources a community looks to for information, (2) whether the information is credible, (3) whether the local government officials tend to be myopic in how they consider the information. Data will be collected using a series of semi-structured interviews over several months with local government officials?mayors, emergency managers and city planners in communities affected by Hurricane Harvey. Previous studies of policy learning have relied on ex-post analyses of government documents to infer the presence and meaning of policy learning. However, this project will gather data in real time, thereby capturing the decision making process for how information is considered and used. Furthermore, it allows local government officials who are engaged in the process of policy learning to speak for themselves and their communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.