The Essential Role of Empirical Validation in Legislative Redistricting Simulation

ABSTRACT

As granular data about elections and voters become available, redistricting simulation methods are playing an increasingly important role when legislatures adopt redistricting plans and courts determine their legality. These simulation methods are designed to yield a representative sample of all redistricting plans that satisfy statutory guidelines and requirements such as contiguity, population parity, and compactness. A proposed redistricting plan can be considered gerrymandered if it constitutes an outlier relative to this sample according to partisan fairness metrics. Despite their growing use, an insufficient effort has been made to empirically validate the accuracy of the simulation methods. We apply a recently developed computational method that can efficiently enumerate all possible redistricting plans and yield an independent sample from this population. We show that this algorithm scales to a state with a couple of hundred geographical units. Finally, we empirically examine how existing simulation methods perform on realistic validation datasets.

KEYWORDS:

• Enumeration
• Gerrymandering
• Graph partition
• Markov chain Monte Carlo
• Redistricting
• Zero-suppressed binary decision diagram

Supplementary Materials

Replication materials and enumeration results are available as Fifield, Imai, et al. (2020).

ORCID

Benjamin Fifield http://orcid.org/0000-0002-2247-0201

Kosuke Imai http://orcid.org/0000-0002-2748-1022

Christopher T. Kenny http://orcid.org/0000-0002-9386-6860

Acknowledgments

We thank Steve Schecter, participants of the Quantitative Gerrymandering and Redistricting Conference at Duke University, and two anonymous reviewers for helpful comments.

Additional information

Funding

Kawahara acknowledges financial support by JSPS KAKENHI grant number JP18K04610. The computations in this article were run on the FASRC Cannon cluster supported by the FAS Division of Science Research Computing Group at Harvard University.