Election forensics is a hot topic for research these days, and recently Arturas Rozenas from NYU published an interesting new paper at Political Analysis (the journal I co-edit). His paper, “Detecting Election Fraud from Irregularities in Vote-Shares” should be of interest to folks studying election integrity and election fraud. Here’s the abstract:
I develop a novel method to detect election fraud from irregular patterns in the distribution of vote-shares. I build on a widely discussed observation that in some elections where fraud allegations abound, suspiciously many polling stations return coarse vote-shares (e.g., 0.50, 0.60, 0.75) for the ruling party, which seems highly implausible in large electorates. Using analytical results and simulations, I show that sheer frequency of such coarse vote-shares is entirely plausible due to simple numeric laws and does not by itself constitute evidence of fraud. To avoid false positive errors in fraud detection, I propose a resampled kernel density method (RKD) to measure whether the coarse vote-shares occur too frequently to raise a statistically qualified suspicion of fraud. I illustrate the method on election data from Russia and Canada as well as simulated data. A software package is provided for an easy implementation of the method.
And since Political Analysis requires that authors provide code and data to replicate the work reported in their paper, here’s the replication materials from Arturas.