Hi! I am a PhD candidate in the Statistics Department at Columbia University, advised by Michael Sobel. My first name is pronounced [Hayn], and my pronouns are they/them.

My research focuses on the intersection of statistics and American politics. For methods, I have been working on developing new measures of ideological and affective polarization using the Wasserstein distance and combining the ideological and social interpretations of roll-call votes using a fused latent factor and network model. Substantively, I am interested in public opinion, polarization, and race, ethnicity, and politics.

Before coming to Columbia, I was at MIT Media Lab’s Opera of the Future group for an MS where I designed interactive AR musical experiences and helped produce hybrid acoustic+digital musical performances. I also received a BS from MIT in Electrical Engineering with a minor in Music.

## News

[May 2024] I will be presenting my paper on the Wasserstein Bipolarization Index at PolMeth 2024.

[May 2023] I will be presenting a poster on the fused latent factor and graphical modeling of roll-call votes at PolMeth 2023.

## Research

*Preprint*. Hane Lee, Michael Sobel (2024). “The Wasserstein Bipolarization Index: A New Measure of Public Opinion Polarization, with an Application to Cross-Country Attitudes toward COVID-19 Vaccination Mandates.”

## Abstract

Although the topic of opinion polarization receives much attention from the media, public opinion researchers and political scientists, the phenomenon itself has not been adequately characterized in either the lay or academic literature. To study opinion polarization among the public, researchers compare the distributions of respondents to survey questions or track the distribution of responses to a question over time using ad-hoc methods and measures such as visual comparisons, variances, and bimodality coefficients. To remedy this situation, we build on the axiomatic approach in the economics literature on income bipolarization, specifying key properties a measure of bipolarization should satisfy: in particular, it should increase as the distribution spreads away from a center toward the poles and/or as clustering below or above this center increases. We then show that measures of bipolarization used in public opinion research fail to satisfy one or more of these axioms. Next, we propose a p-Wasserstein polarization index that satisfies the axioms we set forth. Our index measures the dissimilarity between an observed distribution and a distribution with all the mass clustered on the lower and upper endpoints of the scale. We use our index to examine bipolarization in attitudes toward governmental COVID-19 vaccine mandates across 11 countries, finding the U.S and U.K are most polarized, China, France and India the least polarized, while the others (Brazil, Australia, Columbia, Canada, Italy, Spain) occupy an intermediate position.

*Working paper*. Hane Lee, Andrew Davison, Zhiliang Ying. “A Fused Ideological and Social Model of Roll-Call Votes”.

## Abstract

Political scientists have prioritized ideology as the main driving force of roll-call votes, both in theory and methods. However, legislators have complex motivations, and ideological models alone do not sufficiently explain all variation in votes. Many studies have attempted to study the social motivations behind roll call votes using networks, but these social networks disregard ideology and attribute all variation votes to a single source of social ties, such as cosponsorship or shared committee membership. We propose to empirically integrate the two approaches through a fused latent factor and social network model that primarily attributes the variation among votes to latent factors and the remaining variation not explained by the latent factors to a social network. We apply our model to the 101st Senate and find that 1) the model successfully decomposes ideology and social ties, and 2) the social network captures a complex combination of social ties, integrating multiple social networks that were previously studied.

Chris Andrade, Jonathan Auerbach, Icaro Bacelar, Hane Lee, Angela Tan, Mariana Vazquez, and Owen Ward (2023). “Does it pay to park in front of a fire hydrant?”. *Significance* 20(1), pp. 28–30.

## Teaching

### Instructor (at Columbia University)

- Calculus-based Introduction to Statistics (Summer 2024) [Syllabus]

### Teaching Assistant (at Columbia University)

Graduate

- Probability Theory (Fall 2021, Spring 2021, Fall 2020)
- Statistical Inference (Fall 2023)
- Accelerated Probability Theory/Statistical Inference (Fall 2022)
- Statistical Machine Learning (Spring 2022)
- Linear Regression Models (Spring 2023)
- Bayesian Statistics (Summer 2022)

Undergraduate

- Introduction to Statistics (Spring 2020)
- Introduction to Statistical Reasoning (Fall 2019)