Theoretical Foundations and Empirical Evaluations of Partisan Fairness in District-Based Democracies
Jonathan N. Katz, Gary King, Elizabeth Rosenblatt. 2020.
"Theoretical Foundations and Empirical Evaluations of Partisan Fairness in District-Based Democracies".
American Political Science Review, 114, 1, Pp. 164–178.

Abstract
We clarify the theoretical foundations of partisan fairness standards for district-based democratic electoral systems, including essential assumptions and definitions that have not been recognized, formalized, or in some cases even discussed. We also offer extensive empirical evidence for assumptions with observable implications. 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, or 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 involves complicated politics with numerous participants and conflicting goals, measures biased for partisan fairness sometimes still provide useful descriptions of other aspects of electoral systems.
See Also
- [Dataset] Replication data (Harvard Dataverse)
- [Paper] Data, Analyses, and Reports for the Arizona Independent Redistricting Commission, Filed With the U.S. Department of Justice (2012)
- [Paper] Edited Transcript of a Talk on Partisan Symmetry at the 'Redistricting and Representation Forum' (2018)
- [Paper] How to Conquer Partisan Gerrymandering (2017)
- [Paper] How to Measure Legislative District Compactness If You Only Know It When You See It (2021)
- [Paper] The Future of Partisan Symmetry As a Judicial Test for Partisan Gerrymandering After LULAC V. Perry (2008)
- [Paper] There's a Simple Solution to the Latest Census Fight (2021)
- [Paper] Empirical versus Theoretical Claims about Extreme Counterfactuals: A Response (2009)