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

The results of the 2016 US Presidential Election were, to put it mildly, a surprise. Pre-election polls and forecasts based on these polls pointed to a Clinton victory, a prediction shared by betting markets and pundits. In the aftermath of the vote, the main question asked is "why?" with answers ranging from the political to the economic to the social/cultural. In this article we attempt to provide a preliminary answer to a fundamental question: who voted for Trump, who voted for Clinton, and who voted for a third party or did not vote? By combining data from the United States census with the election results and recently proposed machine learning methods for ecological inference using regressions based on samples from a distribution, we provide local demographic estimates of voting and nonvoting. Unlike with exit polls, we are able to draw conclusions across the entire US and at a local level, about voters and non-voters, for interesting and novel combinations of predictor variables. It is our hope that this analysis will help inform the typical election post mortems, which are usually informed by incomplete information, due the following factors: - Vote counts will not be finalized in many precincts until days or in rare cases weeks after the election. Very close popular vote totals yield winner-take-all results, a fact of the US's electoral system but one that can lead to winner-take-all explanations.