Replication Data for: Partisan Dislocation: A Precinct-Level Measure of Representation and Gerrymandering

We introduce a fine-grained measure of the extent to which electoral districts combine and split local communities of co-partisans in unnatural ways. Our indicator -- which we term Partisan Dislocation -- is a measure of the difference between the partisan composition of a voter's geographic nearest neighbors and that of her assigned district. We show that our measure is a good local and global indicator of district manipulation, easily identifying instances in which districts carve up clusters of co-partisans (cracking) or combine them in unnatural ways (packing). We demonstrate that our measure is related to but distinct from other approaches to the measurement of gerrymandering, and has some clear advantages, above all as a complement to simulation-based approaches, and as a way to identify the specific neighborhoods most affected by gerrymandering. It can also be used prospectively by district-drawers who wish to create maps that reflect voter geography, but according to our analysis, that goal will sometimes be in conflict with the goal of partisan fairness.

本文提出一种细粒度度量方法，用于量化选举选区以非自然方式合并或拆分同党派选民本地社群的程度。我们将该指标命名为党派错位（Partisan Dislocation），其用于衡量选民地理最近邻的党派构成与其所归属选区的党派构成之间的差异。研究表明，该指标可作为选区操纵的有效局部与全局指示器，能够轻松识别出选区拆分同党派选民集群（cracking）或以非自然方式合并集群（packing）的典型案例。本文证明，该指标与现有杰利蝾螈（gerrymandering）度量方法存在关联但又独具特色，且具备多项显著优势：首先可作为基于模拟的度量方法的补充，同时能够精准定位受选区操纵影响最严重的特定社区。此外，该指标还可供希望绘制能够反映选民地理分布的选区地图的区划人员前瞻性使用，但本文分析显示，这一目标有时会与党派公平性目标产生冲突。