2020/05/26 by Daryl DeFord, Moon Duchin, DeFord, Daryl +3 · 1 citation
Economics, Econometrics and Finance · Social Sciences · #05C90 #60J20 #62P25 #91F10 #Applications (stat.AP) #Computers and Society (cs.CY) #Electoral Systems and Political Participation #FOS: Computer and information sciences #Game Theory and Voting Systems #Political Systems and Governance #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2005.12731
openalex publication_date 2020/05/26 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
The recent wave of attention to partisan gerrymandering has come with a push\nto refine or replace the laws that govern political redistricting around the\ncountry. A common element in several states' reform efforts has been the\ninclusion of competitiveness metrics, or scores that evaluate a districting\nplan based on the extent to which district-level outcomes are in play or are\nlikely to be closely contested.\n In this paper, we examine several classes of competitiveness metrics\nmotivated by recent reform proposals and then evaluate their potential outcomes\nacross large ensembles of districting plans at the Congressional and state\nSenate levels. This is part of a growing literature using MCMC techniques from\napplied statistics to situate plans and criteria in the context of valid\nredistricting alternatives. Our empirical analysis focuses on five\nstates---Utah, Georgia, Wisconsin, Virginia, and Massachusetts---chosen to\nrepresent a range of partisan attributes. We highlight situation-specific\ndifficulties in creating good competitiveness metrics and show that optimizing\ncompetitiveness can produce unintended consequences on other partisan metrics.\nThese results demonstrate the importance of (1) avoiding writing detailed\nmetric constraints into long-lasting constitutional reform and (2) carrying out\ncareful mathematical modeling on real geo-electoral data in each redistricting\ncycle.\n