What is a competitive election, anyway? At first it may sound like an obvious question, but it turns out to be hard to specify. Even more subtle is to figure out what exactly we might mean by saying that one districting plan is more competitive than another.
We’ve got a recent preprint looking closely at the kinds of competitiveness-promoting language that is being featured in redistricting reform bills around the country.
In this paper, we examine several classes of competitiveness metrics and then evaluate their potential outcomes across large ensembles of districting plans at the Congressional and state Senate levels. This is part of a growing literature using MCMC techniques from applied statistics to situate plans and criteria in the context of valid redistricting alternatives. Our empirical analysis focuses on five states–Utah, Georgia, Wisconsin, Virginia, and Massachusetts–chosen to represent a range of partisan attributes. We study the feasibility and the partisan consequences of adopting and tightening various kinds of competitiveness requirements. We show that optimizing for competitiveness sometimes pulls partisan metrics to one side of their neutral baseline. These results highlight the importance of investigating possible new rules with careful mathematical modeling on real geospatial data to look for unintended consequences and for tensions with other redistricting goals.