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

Danica J. Sutherland; Seth Flaxman; Yee Whye Teh; Yu-Xiang Wang

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

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arxiv 1611.03787 v2 pith:S2UOO332 submitted 2016-11-11 stat.AP stat.ML

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

Seth Flaxman , Danica J. Sutherland , Yu-Xiang Wang , Yee Whye Teh This is my paper

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keywords censusecologicalinferenceregressiontrumpvotevotingclinton

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We combine fine-grained spatially referenced census data with the vote outcomes from the 2016 US presidential election. Using this dataset, we perform ecological 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 socioeconomic features. Ecological inference allows us to estimate "exit poll" style results like what was Trump's support among white women, but for entirely novel categories. We also perform exploratory data analysis to understand which census variables are predictive of voting for Trump, voting for Clinton, or not voting for either. All of our methods are implemented in Python and R, and are available online for replication.

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