Detecting Anomalies in the 2020 US Presidential Election Votes with Benford’s Law

Abstract

This study applies Benford’s law to detect anomalies in county-level vote data for the 2020 US presidential election. Most prominent distribution violations are observed with Republican vote counts in blue states, all vote counts in states won by the Democratic candidate, and Democratic vote counts in swing states. Distributions are anomalous in swing states won by the Democratic nominee and not anomalous in swing states won by the Republican nominee. The results are robust to two-digit analysis, Monte Carlo simulations of p-values, broad or narrow swing state definitions, and when compared to distributions observed in 2008, 2012, and 2016 elections.

Keywords: US presidential election, Benford's law, election analysis, data irregularities

JEL Classification: C12, D72