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We combine fine-grained spatially referenced census data with the voteoutcomes from the 2016 US presidential election. Using this dataset, we performecological inference using distribution regression (Flaxman et al, KDD 2015)with a multinomial-logit regression so as to model the vote outcome Trump,Clinton, Other / Didn't vote as a function of demographic and socioeconomicfeatures. Ecological inference allows us to estimate "exit poll" style resultslike what was Trump's support among white women, but for entirely novelcategories. We also perform exploratory data analysis to understand whichcensus variables are predictive of voting for Trump, voting for Clinton, or notvoting for either. All of our methods are implemented in Python and R, and areavailable online for replication.

Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata