The Essential Role of Empirical Validation in Legislative Redistricting Simulation. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Fifield, Benjamin; Imai, Kosuke; Kawahara, Jun; Kenny, Christopher T.
• Is Part Of: Statistics and public policy. Volume 7:Issue 1(2020)
• Page Start: 52
• Page End: 68
• Publication Date: 2020-01-01
• Subjects: Enumeration -- Gerrymandering -- Graph partition -- Markov chain Monte Carlo -- Redistricting -- Zero-suppressed binary decision diagram; Policy sciences -- Methodology -- Periodicals; Social sciences -- Statistical methods -- Periodicals; Medical statistics -- Methodology -- Periodicals; Statistics -- Periodicals; Medical statistics -- Methodology; Policy sciences -- Methodology; Social sciences -- Statistical methods; Statistics; Periodicals; 320.60727
• DOI: 10.1080/2330443X.2020.1791773
• Languages: English
• ISSNs: 2330-443X

Abstract

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.