Hane Lee
Hi! I am a postdoc at the University of Chicago Data Science Institute, working with Moon Duchin at the Data and Democracy Lab. I recently received my PhD from the Statistics Department at Columbia University, where I was advised by Michael Sobel. My first name is pronounced [Hayn], and my pronouns are they/them.
My research focuses on the intersection of political methodology and American politics, where I develop measures of collective political preferences and examines their substantive implications in American politics: in public opinion polarization, systems of preferences in legislative and mass politics, and the electoral success of minority groups. A recurring theme in my research is the lack of conceptual clarity and reductive operationalization of these phenomena in current work. My research aims to develop rigorous measurement frameworks rooted in theoretically motivated definitions, leading to principled operationalization of concepts and enabling the revision of established substantive conclusions when existing measures obscure important patterns
My research on public opinion develops new measures of opinion divergence using optimal transport and examines how policy disagreement can contribute to mass polarization independently of partisanship. My work on collective systems of preferences uses an extended latent factor-network model to identify collective legislative voting patterns that are obscured by conventional latent-factor models, revealing that within-party factions and cross-party coalitions constitute distinct forms of legislative behavior. In electoral politics, I study how electoral systems shape opportunities for representation, including measuring group electoral competitiveness and using simulation methods to study the importance of within-group coordination in minority-group representation.
Before 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
[Sep 2026] I will be presenting my paper on measuring congressional social ties at APSA 2026.
[Aug 2026] I will be presenting my paper on the Wasserstein Bipolarization Index at JSM 2026.
[Jul 2026] I will be presenting my poster on opinion disagreement and polarization at PolMeth 2026.
[Jun 2026] I will be presenting my paper on the Wasserstein Bipolarization Index at University of Rochester’s Summer Conference in Applied Methods for Political Science.
Research
Job Market Paper. Hane Lee. “Reconsidering the Necessity of Partisanship in Mass Polarization”.
Abstract
Mass polarization is often considered a partisan phenomenon, but is partisanship a necessary condition for mass polarization? To answer this question, we examine whether policy disagreement, specifically opinion divergence, can contribute to affective polarization independently of partisanship. We test three theoretical and empirical premises of partisan-centric accounts relating to opinion divergence. First, we test whether opinion divergence has remained stable, as previous conclusions of stability were instrumental in leading subsequent theories to de-center policy disagreement and prioritize partisanship. Using the Wasserstein Bipolarization Index that axiomatizes the distributional intuitions of bipolarization, we find increasing opinion divergence in the general electorate contrary to previous findings. Second, we test whether this increase extends beyond partisans by examining non-leaning Independents, whom partisan-centric models assume remain centrist over time. We find that non-leaners diverge comparably to weak partisans and leaning Independents, demonstrating that opinion divergence is not confined to the partisan framework. Third, to confirm that non-leaners' policy opinions have affective consequences, we examine whether they are associated with affective differentiation based on agreement or disagreement and find a positive and growing correlation. Together, these findings provide evidence consistent with opinion divergence operating as a path to affective polarization independently of partisan identity. To reconcile this pathway with existing partisan-centric models, we propose a \textit{substantive-identity dual model} of polarization.\\
Hane Lee and Michael Sobel (2026). “Measuring Public Opinion: “The Wasserstein Bipolarization Index”, with Application to Cross-National Attitudes Toward Mandatory Vaccination for COVID-19.”. Annals of Applied Statistics 20 (2) pp. 1719–1735. https://doi.org/10.1214/26-AOAS2162
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 and Andrew Davison. “Recovering Collective Structure in Congressional Voting: A Fused Latent Factor and Network Approach”.
Abstract
Latent factor models that dominate roll call analysis assume conditional independence across legislators and represent their ideal positions in a linear space. These assumptions structurally prevent the model from encoding collective behavior that does not fit low-dimensional chamber-wide patterns. To model these behaviors, we adopt a fused latent factor and network model that captures the predominant partisan patterns with the latent factors and attribute residual structure to the network. Applying the fused model to the 101st (1989--90) and 114th (2015--16) Senates, we first identify two systematic failures of the latent factor: senators with moderate latent positions are assigned uncertain probabilities on party-line votes, and intra-party split votes expose the latent model's inability to represent factions. The network component corrects both, although the network closes the misprediction gap entirely for party-line votes but only partially for intra-party splits. Next, we apply a degree-corrected stochastic block model to the fitted network and recover interpretable voting blocs in both Senates. The 101st Senate features within-party factions that match historically documented coalitions and are driven by shared issue interests. In the 114th Senate, the blocks take on an entirely different interpretation, uncovering two institutionally opposed coalitions: a cross-party governing bloc, including members of the Republican party leadership, and a Republican insurgent bloc, both driven by opposition of the other. Our findings demonstrate that roll call data alone contains recoverable signals of collective legislative behavior beyond partisanship that cannot be captured by latent factors, and the content of those signals changed substantially across decades of polarization.
Working paper. Yuki Atsusaka, Diana Da In Lee, and Hane Lee. “Quantifying Group-Based Electoral Competitiveness”.
Abstract
How can we measure electoral competitiveness among multiple candidates with varying group affiliations? We propose the concept of group-level electoral competitiveness, which generalizes the conventional margin of victory to any number of candidates and group affiliations while avoiding strong assumptions about perfect coordination among in-group candidates. We demonstrate its applicability in three domains of American politics: racial competitiveness in congressional elections, partisan competitiveness in local elections, and competitiveness across candidates' occupational backgrounds. Our findings reveal patterns of group advantage and disadvantage that are obscured by traditional measures. By extending electoral margins to group-based competition, this paper provides a unified and flexible methodological framework for evaluating long-standing questions in the study of electoral democracy. The proposed measure is implemented via an open-source software R package gmv.
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.