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An Optimization Case Study in Analyzing Missouri Redistricting

Author

Listed:
  • Kiera W. Dobbs

    (Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana–Champaign, Urbana, Illinois 61801)

  • Rahul Swamy

    (Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana–Champaign, Urbana, Illinois 61801)

  • Douglas M. King

    (Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana–Champaign, Urbana, Illinois 61801)

  • Ian G. Ludden

    (Department of Computer Science, University of Illinois Urbana–Champaign, Urbana, Illinois 61801)

  • Sheldon H. Jacobson

    (Department of Computer Science, University of Illinois Urbana–Champaign, Urbana, Illinois 61801)

Abstract

Every 10 years, U.S. states redraw their congressional and state legislative district plans. This process decides the political landscape for the subsequent 10 years. Prior to the 2021 redistricting cycle, Missouri enacted new criteria for state legislative districts. The Missouri League of Women Voters (LWV-MO) contacted the authors to analyze the potential impact of these new criteria on the map-drawing process. We apply recombination (a spanning tree method) within a local search optimization framework to analyze the interplay between political geography, constitutional requirements, and political fairness in Missouri. We use this framework to produce district plans that satisfy the new criteria and prioritize different aspects of fairness. The results, quantified by several measures of fairness, reveal an inherent Republican advantage in Missouri because of the state’s political geography and constitutional requirements. We conclude that Missouri’s political geography and constitutional requirements prevent the optimization framework from substantially improving political fairness in state legislative plans. In contrast, the framework can substantially improve political fairness in Missouri congressional plans, which are not subject to the new requirements. The LWV-MO used this work to advocate for fairness and transparency in their testimonies for the Missouri redistricting commission’s public hearings.

Suggested Citation

  • Kiera W. Dobbs & Rahul Swamy & Douglas M. King & Ian G. Ludden & Sheldon H. Jacobson, 2024. "An Optimization Case Study in Analyzing Missouri Redistricting," Interfaces, INFORMS, vol. 54(2), pages 162-187, March.
  • Handle: RePEc:inm:orinte:v:54:y:2024:i:2:p:162-187
    DOI: 10.1287/inte.2022.0037
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    References listed on IDEAS

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    1. Ricca, Federica & Simeone, Bruno, 2008. "Local search algorithms for political districting," European Journal of Operational Research, Elsevier, vol. 189(3), pages 1409-1426, September.
    2. Saxon, James, 2020. "Reviving Legislative Avenues for Gerrymandering Reform with a Flexible, Automated Tool," Political Analysis, Cambridge University Press, vol. 28(3), pages 372-394, July.
    3. Amariah Becker & Dara Gold, 2022. "The gameability of redistricting criteria," Journal of Computational Social Science, Springer, vol. 5(2), pages 1735-1777, November.
    4. Burcin Bozkaya & Erhan Erkut & Dan Haight & Gilbert Laporte, 2011. "Designing New Electoral Districts for the City of Edmonton," Interfaces, INFORMS, vol. 41(6), pages 534-547, December.
    5. Daryl DeFord & Moon Duchin & Justin Solomon, 2020. "A Computational Approach to Measuring Vote Elasticity and Competitiveness," Statistics and Public Policy, Taylor & Francis Journals, vol. 7(1), pages 69-86, January.
    6. Federica Ricca & Andrea Scozzari & Bruno Simeone, 2013. "Political Districting: from classical models to recent approaches," Annals of Operations Research, Springer, vol. 204(1), pages 271-299, April.
    7. Chen, Jowei & Rodden, Jonathan, 2013. "Unintentional Gerrymandering: Political Geography and Electoral Bias in Legislatures," Quarterly Journal of Political Science, now publishers, vol. 8(3), pages 239-269, June.
    8. Daniel Carter & Zach Hunter & Dan Teague & Gregory Herschlag & Jonathan Mattingly, 2020. "Optimal Legislative County Clustering in North Carolina," Statistics and Public Policy, Taylor & Francis Journals, vol. 7(1), pages 19-29, January.
    9. Nolan McCarty & Keith T. Poole & Howard Rosenthal, 2009. "Does Gerrymandering Cause Polarization?," American Journal of Political Science, John Wiley & Sons, vol. 53(3), pages 666-680, July.
    10. Katz, Jonathan N. & King, Gary & Rosenblatt, Elizabeth, 2020. "Theoretical Foundations and Empirical Evaluations of Partisan Fairness in District-Based Democracies," American Political Science Review, Cambridge University Press, vol. 114(1), pages 164-178, February.
    11. Sarah Cannon & Ari Goldbloom-Helzner & Varun Gupta & JN Matthews & Bhushan Suwal, 2023. "Voting Rights, Markov Chains, and Optimization by Short Bursts," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-38, March.
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