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Colorado in context: Congressional redistricting and competing fairness criteria in Colorado

Author

Listed:
  • Jeanne Clelland

    (University of Colorado Boulder)

  • Haley Colgate

    (University of Wisconsin-Madison)

  • Daryl DeFord

    (Washington State University)

  • Beth Malmskog

    (Colorado College)

  • Flavia Sancier-Barbosa

    (Colorado College)

Abstract

In this paper, we apply techniques of ensemble analysis to understand the political baseline for Congressional representation in Colorado. We generate a large random sample of reasonable redistricting plans and determine the partisan balance of each district using returns from state-wide elections in 2018, and analyze the 2011/2012 enacted districts in this context. Colorado recently adopted a new framework for redistricting, creating an independent commission to draw district boundaries, prohibiting partisan bias and incumbency considerations, requiring that political boundaries (such as counties) be preserved as much as possible, and also requiring that mapmakers maximize the number of competitive districts. We investigate the relationships between partisan outcomes, number of counties which are split, and number of competitive districts in a plan. This paper also features two novel improvements in methodology—a more rigorous statistical framework for understanding necessary sample size, and a weighted-graph method for generating random plans which split approximately as few counties as acceptable human-drawn maps.

Suggested Citation

  • Jeanne Clelland & Haley Colgate & Daryl DeFord & Beth Malmskog & Flavia Sancier-Barbosa, 2022. "Colorado in context: Congressional redistricting and competing fairness criteria in Colorado," Journal of Computational Social Science, Springer, vol. 5(1), pages 189-226, May.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00119-7
    DOI: 10.1007/s42001-021-00119-7
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Gregory Herschlag & Han Sung Kang & Justin Luo & Christy Vaughn Graves & Sachet Bangia & Robert Ravier & Jonathan C. Mattingly, 2020. "Quantifying Gerrymandering in North Carolina," Statistics and Public Policy, Taylor & Francis Journals, vol. 7(1), pages 30-38, January.
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