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.