Abstract
ABSTRACTThe 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 article, 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.
Highlights
In 2018 alone, five states saw redistricting reform measures passed by voters at the ballot box, signaling a larger movement to revisit redistricting rules in state laws and constitutions in the run-up to the 2020 Census
With these questions in mind, in this paper we develop statistical techniques to probe the consequences of various ways to quantify competitiveness of districting plans
Missouri has adopted a definition of competitiveness for redistricting that requires the State Demographer to compute a metric called the efficiency gap for any proposed districting plan, with respect to a particular synthetic vote pattern created by a weighted combination of recent election outcomes
Summary
The MIT Faculty has made this article openly available. Please share how this access benefits you.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.