About Me

Welcome! I am a PhD Candidate in the Department of Political Science at the University of North Carolina at Chapel Hill. I earned my M.A. in Political Science from the University of North Carolina at Chapel Hill in 2018 and my B.A. in Political Science and International Affairs from the University of Georgia in 2016. My research interests include American politics, U.S. Congress, political parties, and elections. Most recently, my research has focused on how differences in gender and political experience affect candidate self-presentation in primary elections. This work is forthcoming in Political Research Quarterly.

In my dissertation, I explore the dynamics of modern campaigns for the House of Representatives. Over the past several decades, American congressional elections have transformed from campaigns centered around local issues into nationally-oriented—or “nationalized”—contests. However, I demonstrate that all congressional elections are not national and that some candidates still “go local,” reacting to their specific constituency and taking up issues important to their district. To that end, I show that theories of strategic candidate behavior must be updated to better reflect what locally-oriented campaigning looks like in today’s era of nationalized politics.

To measure the degree to which a candidate’s campaign is locally-oriented, I embark on an ambitious data collection effort. I have compiled an original data set of text on issue positions and platform policies from congressional candidate campaign websites. These data are unique in that they provide a near complete picture of modern campaigns for Congress. Once completed, this data set of nearly 5,000 campaign platforms will constitute the first comprehensive collection of text on congressional candidate issue agendas. Employing these data, I investigate if and how candidates discuss important issues-of-the-day like the Opioid Epidemic and the #MeToo movement in terms of their local constituency. To address my research questions, my dissertation makes use of methods like structural topic modeling, entropy balancing, and random forest classification.