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

Jeanne Clelland (), Haley Colgate, Daryl DeFord, Beth Malmskog and Flavia Sancier-Barbosa
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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

Journal of Computational Social Science, 2022, vol. 5, issue 1, No 9, 189-226

Abstract: 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.

Keywords: Redistricting; Colorado; Ensemble analysis; MCMC; Spanning trees; Two-sample KS statistic (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s42001-021-00119-7

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