Quantifying Gerrymandering in North Carolina

Abstract

By comparing a specific redistricting plan to an ensemble of plans, we evaluate whether the plan translates individual votes to election outcomes in an unbiased fashion. Explicitly, we evaluate if a given redistricting plan exhibits extreme statistical properties compared to an ensemble of nonpartisan plans satisfying all legal criteria. Thus, we capture how unbiased redistricting plans interpret individual votes via a state's geopolitical landscape. We generate the ensemble of plans through a Markov chain Monte Carlo algorithm coupled with simulated annealing based on a reference distribution that does not include partisan criteria. Using the ensemble and historical voting data, we create a null hypothesis for various election results, free from partisanship, accounting for the state's geo-politics. We showcase our methods on two recent congressional districting plans of NC, along with a plan drawn by a bipartisan panel of retired judges. We find the enacted plans are extreme outliers whereas the bipartisan judges' plan does not give rise to extreme partisan outcomes. Equally important, we illuminate anomalous structures in the plans of interest by developing graphical representations which help identify and understand instances of cracking and packing associated with gerrymandering. These methods were successfully used in recent court cases. Supplementary materials for this article are available online.

How differential privacy will affect our understanding of health disparities in the United States

“…Other areas where the accuracy of these data are crucial include, but are not limited to, electoral demography (32)(33)(34) and informing business decisions (35). These data are also crucial for expert witness analysis on cases of gerrymandering (36)(37)(38). In addition, census data constitute the most reliable source of information used to validate estimation techniques and the coverage of administrative records (39), which are increasingly being used for research and policy decision-making (40)(41)(42).…”

A Computational Approach to Measuring Vote Elasticity and Competitiveness

“…Each node is decorated with the population of the corresponding precinct and its vote counts with respect to the election , so the population and partisan performance of each district can be computed by summing over the nodes. This discrete approach has enabled a growing literature on sampling techniques in court cases and in scientific literature (Chen and Rodden 2013;Chikina, Frieze, and Pegden 2017;Herschlag et al 2018;DeFord, Duchin, and Solomon 2019).…”

“…Since the set of valid plans typically is too large to enumerate, Markov chain Monte Carlo (MCMC) methods are standard techniques in applied mathematics and statistics for sampling in these situations, used to gather representative samples from the space of interest. Markov chain sampling is currently used by several groups of researchers to evaluate the partisan nature of proposed or enacted districting plans in court challenges (Chikina, Frieze, and Pegden 2017;Herschlag, Ravier, and Mattingly 2017;Herschlag et al 2018;Chikina et al 2019;DeFord, Duchin, and Solomon 2019;Fifield et al 2020). These methods work by constructing large ensembles of plans that satisfy the rules set forth in each state and comparing the statistics of enacted plans to aggregate statistics of the distribution.…”

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“…The most reliable signature of a gerrymandered districtings, then, may well be exactly the kind of partisan fragility of a districting that our test is based on. In particular, this kind of reasoning explains why even groups that are at the cutting edge of developing trustworthy global samplers are nevertheless sometimes also interested in using those samplers to examine local redistrictings of a state (Herschlag et al 2018).…”