Locating the Representational Baseline: Republicans in Massachusetts

Abstract

Republican candidates often receive between 30% and 40% of the two-way vote share in statewide elections in Massachusetts. For the last three Census cycles, MA has held 9-10 seats in the House of Representatives, which means that a district can be won with as little as 6% of the statewide vote. Putting these two facts together, one may be surprised to learn that a Massachusetts Republican has not won a seat in the U.S. House of Representatives since 1994. We argue that the underperformance of Republicans in Massachusetts is not attributable to gerrymandering, nor to the failure of Republicans to field House candidates, but is a structural mathematical feature of the distribution of votes. For several of the elections studied here, there are more ways of building a valid districting plan than there are particles in the galaxy, and every one of them will produce a 9-0 Democratic delegation. arXiv:1810.09051v1 [physics.soc-ph]

Fairmandering: A column generation heuristic for fairness-optimized political districting

“…The political geography of a state, typically defined by a few urban concentrations of more liberal voters punctuating larger and sparser regions of more conservative voters, does not guarantee a fair solution for any reasonable definition of fair. This greatly complicates creating a unified definition of fairness when the number of districts and distribution of voters greatly constraints the space of expected outcomes [11]. We use the efficiency gap [39] as our primary metric because it has gained traction as a standard metric in redistricting lawsuits, has an intuitive and compelling interpretation, and is simple to embed in an integer program as we have shown in Section 2.3.…”

The impact of highly compact algorithmic redistricting on the rural-versus-urban balance

“…In previous work by Chen and Rodden [2] exploring the electoral outcomes of algorithmically generated districts, they nd split cities to be rare in their model. Their work, and related results using MCMC, builds districts by randomly combining precincts [2,7,9]. Chen and Rodden [2], for instance, argue their maps are compact since they combine precincts that are nearest to each other to generate district plans.…”

“…We use 2010 census block data to build United States House of Representative districts using the power diagram capacitated kmeans algorithm. Many previous works use signi cantly more coarse datasets for district building such as precincts [2,7,9]. We allow for some census blocks lying on the border of districts to split their population.…”

A Computational Approach to Measuring Vote Elasticity and Competitiveness

“…One straightforward observation is that there must be some districts at least as far from 50% as the state average. Additionally, as shown in Duchin et al (2019), discretization of geographical units can play a large role in determining the range of possible outcomes, since the voting population is not infinitely divisible.…”

“…The similarity between our results and theirs, however, suggests that our simple optimization heuristics have some power. An interesting direction for future work would be to use the precinct-sorting methods from Duchin et al (2019) to get rigorous upper bounds on the possible number of competitive districts. Comparing to the boxplots of Figure 3 gives a measure of how far these optimized plans are from those in the neutral ensemble.…”