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

15 Pages Posted: 11 Dec 2020

See all articles by Savva Shanaev

Savva Shanaev

Northumbria University

Arina Shuraeva

University of London; University of Northumbria at Newcastle

Binam Ghimire

University of Northumbria at Newcastle

Date Written: November 11, 2020

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

Suggested Citation

Shanaev, Savva and Shuraeva, Arina and Shuraeva, Arina and Ghimire, Binam, Detecting Anomalies in the 2020 US Presidential Election Votes with Benford’s Law (November 11, 2020). Available at SSRN: https://ssrn.com/abstract=3728626 or http://dx.doi.org/10.2139/ssrn.3728626

Savva Shanaev (Contact Author)

Northumbria University ( email )

Pandon Building
208, City Campus East-1
Newcastle-Upon-Tyne, Newcastle NE1 8ST
United Kingdom

Arina Shuraeva

University of London ( email )

Senate House
Malet Street
London, WC1E 7HU
United Kingdom

University of Northumbria at Newcastle ( email )

Pandon Building
208, City Campus East-1
Newcastle-Upon-Tyne, Newcastle NE1 8ST
United Kingdom

Binam Ghimire

University of Northumbria at Newcastle ( email )

Pandon Building
208, City Campus East-1
Newcastle-Upon-Tyne, Newcastle NE1 8ST
United Kingdom

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