Dr. Lee, Department of Political Science
As a political scientist who studies disasters and crises, I have conducted interdisciplinary research on how politics is associated with resilience at different levels of a society. This particular project has been designed to answer the following research question: Does people’s political participation make communities more resilient against disasters overtime? (If so, how?) To partially answer the question, this study focuses on 3145 counties across the United States. There are two hypotheses. First, based on previous social science studies, this study assumes that political leaders strive to solve community problems leading to an increase of disaster resilience when the constituents are politically active. Therefore, we hypothesize that “counties with high voter turnouts become more resilient than counties with low voter turnouts over time.” Second, in line with the previous hypothesis, it is reasonable to assume that political division and fighting can decrease community’s resilience and capabilities. Therefore, we hypothesize that “politically divided counties demonstrate low resilience than non-divided counties.”
To test these hypotheses, we will analyze three public datasets. The first is the Emergency Event Database (EM-DAT), which is a widely used natural disaster database. According to EM-DAT, natural disasters have been increasing since the 1960s across the United States. Our preliminary analysis says that the United States has experienced 3819 natural disasters, and Texas is ranked number one with 330 disasters, followed by Oklahoma, Missouri, Illinois, and California. We will continue to analyze this dataset to find any patterns in disaster events in the United States. The second dataset that will be used is “Baseline Resilience Indicators for Communities.” To prepare for natural disasters, Federal Emergency Management Agency (FEMA) and the University of South Carolina have published resilience scores for all 3145 US counties in 2010, 2015, and 2020. These scores have been published as a database called Baseline Resilience Indicators for Communities (BRIC). The BRIC measured 49 indicators of resilience and grouped them into 6 categories: (1) social resilience, (2) housing/infrastructural resilience, (3) community capital, (4) economic resilience, (5) institutional resilience, (6) and environmental resilience. We will calculate the average resilience score of 3145 US counties, and break down the average resilience score by the six categories: Community Capital, Institutional, Housing/Infrastructural Resilience, Social, Environmental, and Economic Resilience categories. Thirdly, we will obtain a nationwide election data from the National Neighborhood Data Archive (NaNDA). This dataset provides voter turnouts for all counties in the United States and each county’s Democratic and Republican partisanship, known as partisan index. In addition to analyzing the databases, we will create maps using QGIS to show the change of resilience overtime. We, as students of political science, wanted to know how these changes are statistically associated with the residents’ political participation. Especially, with their voter turnouts and partisanship.
We expect both hypotheses to be approved at least partially. Our preliminary analysis tells us that voter turnouts in presidential elections are associated with resilience scores, but voter turnouts in midterm elections did not show any significant results. We need to know why midterm elections did not show such results. We also expect that politically-divided counties are less resilient than counties with strong partisanship. The result will tell us how political division and competition affect community resilience over time.