Nonparametric Detection of Gerrymandering in Multiparty Elections

Abstract

Partisan gerrymandering, i.e., manipulation of electoral district boundaries for political advantage, is one of the major challenges to election integrity in modern day democracies. Yet most of the existing methods for detecting partisan gerrymandering are narrowly tailored toward fully contested two-party elections, and fail if there are more parties or if the number of candidates per district varies (as is the case in many plurality-based electoral systems outside the United States). We propose two methods, based on nonparametric statistical learning, that are able to deal with such cases. The use of multiple methods makes the proposed solution robust against violation of their respective assumptions. We then test the proposed methods against real-life data from national and subnational elections in 17 countries employing the FPTP system.