Redistricting for Proportionality

Author:

Duchin Moon1,Schoenbach Gabe2

Affiliation:

1. Department of Mathematics and Tisch College of Civic Life, Tufts University , Medford , MA 02155 , USA

2. Department of Computer Science , University of Chicago , Chicago , IL , USA

Abstract

Abstract American democracy is currently heavily reliant on plurality in single-member districts, or PSMD, as a system of election. But public perceptions of fairness are often keyed to partisan proportionality, or the degree of congruence between each party’s share of the vote and its share of representation. PSMD has not tended to secure proportional outcomes historically, partially due to gerrymandering, where line-drawers intentionally extract more advantage for their side. But it is now increasingly clear that even blind PSMD is frequently disproportional, and in unpredictable ways that depend on local political geography. In this paper we consider whether it is feasible to bring PSMD into alignment with a proportionality norm by targeting proportional outcomes in the design and selection of districts. We do this mainly through a close examination of the “Freedom to Vote Test,” a redistricting reform proposed in draft legislation in 2021. We find that applying the test with a proportionality target makes for sound policy: it performs well in legal battleground states and has a workable exception to handle edge cases where proportionality is out of reach.

Publisher

Walter de Gruyter GmbH

Subject

General Social Sciences,Sociology and Political Science

Reference22 articles.

1. Benadè, G., A. Procaccia, and J. T. Foltz. You Can Have Your Cake and Redistrict it Too. Preprint. Also available at https://www.gerdusbenade.com/files/21_gt.pdf.

2. Bycoffe, A., E. Koeze, D. Wasserman, and J. Wolfe. The Atlas of Redistricting. https://projects.fivethirtyeight.com/redistricting-maps/ (accessed January 25, 2018).

3. Campisi, M., T. Ratliff, S. Somersille, and E. Veomett. “Geography and Election Outcome Metric: An Introduction.” Election Law Journal 21 (3): 2022–219.

4. Cannon, S., A. Goldbloom-Helzner, V. Gupta, J. N. Matthews, and B. Suwal. 2022. “Voting Rights, Markov Chains, and Optimization by Short Bursts.” Methodology and Computing in Applied Probability. to appear.

5. Cover, B. P. 2018. “Quantifying Partisan Gerrymandering: An Evaluation of the Efficiency Gap Proposal.” Stanford Law Review 70: 1131.

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1. Political Districting;Encyclopedia of Optimization;2023

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