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Theoretical Foundations and Empirical Evaluations of Partisan Fairness in District-Based Democracies

Published online by Cambridge University Press:  23 October 2019

JONATHAN N. KATZ
Affiliation:
California Institute of Technology
GARY KING
Affiliation:
Harvard University
ELIZABETH ROSENBLATT
Affiliation:
Harvard University

Abstract

We clarify the theoretical foundations of partisan fairness standards for district-based democratic electoral systems, including essential assumptions and definitions not previously recognized, formalized, or in some cases even discussed. We also offer extensive empirical evidence for assumptions with observable implications. We cover partisan symmetry, the most commonly accepted fairness standard, and other perspectives. Throughout, we follow a fundamental principle of statistical inference too often ignored in this literature—defining the quantity of interest separately so its measures can be proven wrong, evaluated, and improved. This enables us to prove which of the many newly proposed fairness measures are statistically appropriate and which are biased, limited, or not measures of the theoretical quantity they seek to estimate at all. Because real-world redistricting and gerrymandering involve complicated politics with numerous participants and conflicting goals, measures biased for partisan fairness sometimes still provide useful descriptions of other aspects of electoral systems.

Type
Research Article
Copyright
Copyright © American Political Science Association 2019 

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Footnotes

All appendices are available at GaryKing.org/symmetry; all data and information necessary to replicate our results are available in Katz, King, and Rosenblatt (2019). Our thanks to Steve Ansolabehere, Peter Aronow, Robin Best, Jowei Chen, Shawn Donahue, Moon Duchin, Jon Eguia, Andrew Gelman, Kosuke Imai, Jonathan Krasno, Eric Lander, Daniel Magleby, Michael D. McDonald, Eric McGhee, John Nagle, Nate Persily, Jameson Quinn, Nick Stephanopoulos, Tyler VanderWeele, Sam Wang, Greg Warrington, and Xiang Zhou for their helpful comments.

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