A Computational Approach to Measuring Vote Elasticity and Competitiveness

TL;DR

This paper evaluates various competitiveness metrics for redistricting plans using MCMC methods across five states, highlighting challenges and unintended effects of optimizing for competitiveness in electoral districting.

Contribution

It introduces a computational framework for assessing competitiveness metrics and analyzes their impacts, emphasizing the need for careful modeling in redistricting reforms.

Findings

Optimizing competitiveness can negatively affect partisan fairness.

Creating effective competitiveness metrics is complex and context-dependent.

Careful mathematical modeling is essential for fair redistricting.

Abstract

The recent wave of attention to partisan gerrymandering has come with a push to refine or replace the laws that govern political redistricting around the country. A common element in several states' reform efforts has been the inclusion of competitiveness metrics, or scores that evaluate a districting plan based on the extent to which district-level outcomes are in play or are likely to be closely contested. In this paper, we examine several classes of competitiveness metrics motivated by recent reform proposals and then evaluate their potential outcomes across large ensembles of districting plans at the Congressional and state Senate levels. This is part of a growing literature using MCMC techniques from applied statistics to situate plans and criteria in the context of valid redistricting alternatives. Our empirical analysis focuses on five states---Utah, Georgia, Wisconsin, Virginia, and Massachusetts---chosen to represent a range of partisan attributes. We highlight situation-specific difficulties in creating good competitiveness metrics and show that optimizing competitiveness can produce unintended consequences on other partisan metrics. These results demonstrate the importance of (1) avoiding writing detailed metric constraints into long-lasting constitutional reform and (2) carrying out careful mathematical modeling on real geo-electoral data in each redistricting cycle.