The Economics of Partisan Gerrymandering

TL;DR

This paper models the strategic behavior of partisan gerrymandering, showing how uncertainty types influence optimal districting strategies and providing empirical evidence from US elections.

Contribution

It introduces a formal model linking gerrymandering strategies to information design, revealing how uncertainty shapes optimal districting plans and analyzing real-world data.

Findings

Idiosyncratic uncertainty dominates in US elections, favoring opponent packing.

Traditional pack-and-crack districting is approximately optimal under real-world conditions.

Optimal districting strategies depend on the dominant type of electoral uncertainty.

Abstract

We study the problem of a partisan gerrymanderer who assigns voters to equipopulous districts so as to maximize his party's expected seat share. The designer faces both aggregate uncertainty (how many votes his party will receive) and idiosyncratic, voter-level uncertainty (which voters will vote for his party). We argue that pack-and-pair districting, where weaker districts are ``packed'' with a single type of voter, while stronger districts contain two voter types, is typically optimal for the gerrymanderer. The optimal form of pack-and-pair districting depends on the relative amounts of aggregate and idiosyncratic uncertainty. When idiosyncratic uncertainty dominates, it is optimal to pack opposing voters and pair more favorable voters; this plan resembles traditional ``packing-and-cracking.'' When aggregate uncertainty dominates, it is optimal to pack moderate voters and pair extreme voters; this ``matching slices'' plan has received some attention in the literature. Estimating the model using precinct-level returns from recent US House elections indicates that, in practice, idiosyncratic uncertainty dominates and packing opponents is optimal; moreover, traditional pack-and-crack districting is approximately optimal. We discuss implications for redistricting reform and political polarization. Methodologically, we exploit a formal connection between gerrymandering -- partitioning voters into districts -- and information design -- partitioning states of the world into signals.