This user does not wish to be contacted by the media at this time.
Hannah Paul, Ph.D.
hannahpaulphd@gmail.com
Assistant Professor
University of Missouri-Columbia
Year of PhD: 2022
City: Columbia, Missouri
Country: United States
About Me:
Hi! I am an Assistant Professor of Comparative Public Policy at the Truman School of Government and Public Affairs at the University of Missouri. I earned a PhD in Political Science at the University of Colorado Boulder in 2022. My research focuses on the causes and consequences of including underrepresented groups in democratic politics. I study the factors that promote the political integration of immigrant-origin people from the perspective of immigrant behavior and native-born public opinion. I also study the effects of increases in women's political representation. My methodological research focuses on the implementation of models for pooled time series data in political science, especially machine learning techniques. My work has been published in journals such as Political Science Research and Methods, Polity, Social Science Quarterly and Legislative Studies Quarterly. You can visit my Google Scholar Page here! Email me at hannah.paul@missouri.edu.
Research Interests
Immigration & Citizenship
Comparative Political Behavior
Time Series Analysis
Women's Political Representation
Countries of Interest
Sweden
United States
Germany
Publications:
Journal Articles:
Machine learning models, especially ensemble and tree-based approaches, offer great promise to legislative scholars. However, they are heavily underutilized outside of narrow applications to text and networks. We believe this is because they are difficult to interpret: while the models are extremely flexible, they have been criticized as “black box” techniques due to their difficulty in visualizing the effect of predictors on the outcome of interest. In order to make these models more useful for legislative scholars, we introduce a framework integrating machine learning models with traditional parametric approaches. We then review three interpretative plotting strategies that scholars can use to bring a substantive interpretation to their machine learning models. For each, we explain the plotting strategy, when to use it, and how to interpret it. We then put these plots in action by revisiting two recent articles from Legislative Studies Quarterly.
Objectives Despite the frequent use of time series models in the social sciences, they have often remained within the confines of assuming purely linear dynamic effects. We contend that many theories involve relationships that are inherently non-linear. Methods We discuss several approaches to modeling a variety of these types of non-linear autoregressive data-generating processes, specifically threshold effects. Results We replicate and extend a recent analysis, and show evidence of threshold processes. Conclusion In doing so, we show that threshold models allow us to test richer, more complex theoretical implications about dynamic effects.
Decades of research has debated whether women first need to reach a “critical mass” in the legislature before they can effectively influence legislative outcomes. This study contributes to the debate using supervised tree-based machine learning to study the relationship between increasing variation in women's legislative representation and the allocation of government expenditures in three policy areas: education, healthcare, and defense. We find that women's representation predicts spending in all three areas. We also find evidence of critical mass effects as the relationships between women's representation and government spending are nonlinear. However, beyond critical mass, our research points to a potential critical mass interval or critical limit point in women's representation. We offer guidance on how these results can inform future research using standard parametric models.
Do immigrant-origin individuals think about politics in the same way as native-born individuals? Do such attitudinal patterns change over time? We assess immigrants’ level of integration into politics by studying attitudinal differences between immigrant-origin and native-born individuals. We find that immigrants and native-born individuals assign different levels of salience to certain key issues, such as economic development and immigration, but not to others, such as crime. We also find that observed differences between groups attenuate over time. Salience perceptions can differ across groups, but inter-group salience gaps erode for societal issues over time, suggesting that issue salience convergence across groups can be considered an indicator of integration between those with deep roots in a society and relative newcomers.