Current Projects


Modeling Latent Effect Heterogeneity

This project seeks to develop semi-parametric Bayesian regression models to estimate latent heterogeneity in the effect of treatment variables and/or observed covariates. In generalized linear models, omitting interactions can lead to latent occurrences Simpson’s Paradox, which is a long-standing problem in statistical analysis in general and in the social sciences in particular. Simpson’s paradox refers to the possibility that an effect found when data are aggregated is entirely different or even reversed when data are separated and analyzed in groups. If these groups are latent, classical empirical approaches (GLM, mixed models, etc.) are not able to detect and deal with them, meaning that Simpson’s Paradox goes unnoticed by the researcher. In practice, it means that a researcher can conclude that an effect is positive when, in fact, it is positive only for a subgroup of the population but negative for another subgroup. I have used these models to study the latent structure of attitudes toward welfare policies, minority groups, and support for populism in the USA and OECD countries. I show that there is a hidden polarization among the observed socioeconomic groups in some countries but not others. My research indicates that one side effect of welfare policies in highly unequal societies with fragmented party systems is the existence of latent polarization in welfare policy preferences among individuals with similar observed socioeconomic characteristics.


Perceptions, Political Behavior, Polarization, and Welfare Attitudes (with Rob Franzese)

This project examines how socioeconomic positions of the individuals impact their perceptions about the mechanisms of reproduction of social inequalities, and how those perceptions affect their political behaviour. It seeks to understand how socioeconomic-position-dependent perceptions about the socioeconomic environment make some individuals but not others more susceptible to certain populist ‘otherizing’ appeals.



Information, Income Distribution, and Policy Preferences

Using nation-wide surveys and surveys experiments, this project investigates how different social groups process information about inequality and income distribution, how information affect their perceptions, and how those perceptions affect policy preferences. Classical theories about public preferences for the allocation of political authority in multilevel polities emphasize the role of economic conditions, identity, and nationalist/regionalist values. In developed nations, immigration has played a significant role in public attitudes about supra-national integration. This project seeks to understand how information about inequality and income distribution affect political attitudes about welfare policies and political integration. How do different social groups process information about inequality? How does that information change their perception about their social and political environment?

As part of this project, two nation-wide surveys and one survey experiment have already been conducted in Brazil in collaboration with Profa. Marta Arretche, Prof. Rogerio Schlegel, and the Center for Metropolitan Studies (CEM).

Unsupervised Learning Methods and FMM for Election Forensics (with Walter Mebane)

Elections are one of the cornerstones of modern democracies. *Election forensics* is a subarea of Political Science that uses statistical methods to investigate frauds in election.

Frauds are by their very nature concealed phenonema. The perpetrators don’t want to reveal their act and want to avoid leaving any indication of manipulaiton of the results. This NFS-funded project, with Walter Mebane (PA), proposes some unsupervised learning methods and a series of finite mixture models to estimate the probability of fraud in elections using election data.