Together with Jamie
Wheeler, I am studying economic and political predictors of revolution. I am taking various measures of well-being
and applying them within the framework of a model of revolution I developed
during a previous semester. My hope is to better predict instability in
developing countries, which would have applications in fields from diplomacy to
finance.
I became interested in
this project while studying at Trinity College in Oxford, during a tutorial in
comparative politics. I studied both revolution and regime failure, and noticed
that many theories were more explanatory than predictive. These studies often were based on case
studies, and I believe a large-n study (even with a large error coefficient)
can help guide future studies.
I am planning on
pursuing a PhD in Political Science.
Obviously, there is no better way to prepare for graduate school than
bolstering my resume with successful research, and hopefully conference
presentations or a paper publication. After grad school, I hope to either teach
at a university, work in a think-tank, or work as a policy advisor. Any of these career paths would also be aided
by having published works and research experience.
I spend most my research
time poring over databases and looking for data that most closely resembles the
variables within the theoretical model.
I hold weekly meetings with my mentor, where we cover what I have
learned or want to focus on for the next week.
We often spend large portions of this time debating the scope and
structure of the project. Coming from
different academic fields, we often look to apply vastly different tools to
solve the problems in front of us. These differences can lively discussions,
but also allow us to take a unique approach to a large, oft-studied issue in
political science.
This week, I discovered
several statistical methods to deal with the real-world challenges of measuring
well-being in states that are, by definition, politically unstable. While data
is often incomplete or must be estimated in many cases, careful selection of
sources can provide results that are precise, but inaccurate. However, the direction of these inaccuracies
is often similar, allowing this variability to become baked into the error term
of the model. While this reduces the
predictive power of the model, it allows the model to hopefully apply to a
larger number of cases.
