Individual and differential harm in redistricting

Social scientists have developed dozens of measures for assessing partisan bias in redistricting. However, these measures are not easily adapted to other groups, including groups defined by race, class or geography, nor are they applicable to single- or no-party contexts, such as local redistricting. Here we propose a unified framework of harm for evaluating the impacts of a districting plan on individual voters and the groups to which they belong, to overcome these limitations. We consider a voter harmed if their chosen candidate is not elected under the current plan but would be under a different plan. Harm improves on existing measures by both focusing on the choices of individual voters and directly incorporating counterfactual plans. We discuss strategies for estimating harm using redistricting simulations, and demonstrate the utility of our framework through three applications to US redistricting. Overall, harm provides a flexible way to precisely quantify redistricting’s individual impacts. McCartan and Kenny develop a framework for quantifying the individual-level impacts of redistricting in the USA, showing its applicability for identifying partisan gerrymandering and differential effects across populations.