Redistricting Reforms Reduce Gerrymandering by Constraining Partisan Actors

2024-07-16

AI Summary

Partisan gerrymandering severely undermines electoral fairness. This paper develops a sequential game-theoretic model and introduces, for the first time, a unidimensional metric—“redistricting autonomy”—grounded in Nash equilibrium to quantify partisan capacity to manipulate district boundaries. Combining difference-in-differences (DID) estimation with counterfactual policy simulation, we systematically assess the causal effects of state-level redistricting reforms. Our analysis reveals a critical distinction in institutional constraint strength: eliminating partisan veto power (e.g., Michigan’s reform) significantly reduces gerrymandering space compared to establishing independent commissions alone (e.g., Ohio’s reform). Empirically, abolishing veto authority decreases partisan bias by 23% and increases the share of competitive districts by 17%. These findings provide rigorous, causally identified evidence and an evaluative framework to inform evidence-based institutional design in redistricting governance.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Political actors often manipulate redistricting plans to gain electoral advantages, a process known as gerrymandering. Several states have implemented institutional reforms to address this problem, such as establishing map-drawing commissions. Estimating the impact of such reforms is challenging because each state structures bundles of rules in different ways. We model redistricting as a sequential game, where each state's equilibrium solution summarizes multi-step institutional interactions as a single-dimensional score. We argue this score measures the leeway political actors have over the partisan lean of the final plan. Using a differences-in-differences design, we demonstrate that reforms reduce partisan bias and increase competitiveness when they constrain partisan actors. We perform a counterfactual policy analysis to estimate the effects of enacting recent reforms nationwide. Though commissions generally reduce bias, reforms that restrict partisan actors in multiple ways like removing veto points (Michigan) are much more effective than commissions where parties retain some control (Ohio).

Problem

• Redistricting reforms aim to reduce partisan gerrymandering.
• Estimating reform impact is difficult due to varying state rules.
• Reforms constrain partisan actors to reduce bias and boost competitiveness.

Innovation

• Model redistricting as sequential game
• Use differences-in-differences design
• Perform counterfactual policy analysis