I am a doctoral candidate at the University of Maryland, College Park. My research broadly deals with electoral accountability and political polarization in the United States. I have been involved in a number of projects that seek to determine how citizens react to the actions of elected officials in an environment where political attitudes are increasingly influenced by overlapping social identities.
My dissertation, “Empathy and Electoral Accountability,” addresses the topic of political compassion. I argue that prior scholarship has not paid enough attention to perceptions that a politician truly cares for others, which are of particular importance to crucial swing voters. I also develop a classification scheme for the sources of commonality between citizens and politicians, including shared experience, shared emotion, and shared identity. These connections are critical in proving to citizens that a politician is truly empathetic and deserving of support. In related research (published in Political Behavior), my colleagues and I find that many Americans are unaware that Donald Trump was born wealthy, and this misperception leads them to view him as more empathetic toward the common person and more competent a businessman.
Beyond my dissertation project, I have been involved in research projects seeking to understand how Americans respond to a president who backs down from a foreign adversary (published in The Journal of Politics). I am working on a project that shows how transitory self-esteem (such as a sudden drop or increase) is linked directly with the strength of one’s social identities.
Finally, I am working on a number of projects in the field of survey methodology. My colleagues and I developed an honesty pledge that significantly reduced vote overreporting in the 2014 election (published in Electoral Studies). I also worked as the Research Methodology Fellow with the Washington Post’s Polling Division in 2018 and am currently working with them on research that identifies the most accurate likely voter models.