``Redistricting Reforms Reduce Gerrymandering by Constraining Partisan Actors.''

 

  Abstract

Political actors frequently manipulate redistricting plans to gain electoral advantages, a process commonly known as gerrymandering. To address this problem, several states have implemented institutional reforms including the establishment of map-drawing commissions. It is difficult to assess the impact of such reforms because each state structures bundles of complex rules in different ways. We propose to model redistricting processes as a sequential game. The equilibrium solution to the game summarizes multi-step institutional interactions as a single dimensional score. 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 partisan effects of enacting recent institutional reforms nationwide. We find that instituting redistricting commissions generally reduces the current Republican advantage, but Michigan-style reforms would yield a much greater pro-Democratic effect than types of redistricting commissions adopted in Ohio and New York.

  Related Papers and Software

Kenny, Christopher T., Cory McCartan, Tyler Simko, Shiro Kuriwaki, and Kosuke Imai. (2023). ``Widespread Partisan Gerrymandering Mostly Cancels Nationally, but Reduces Electoral Competition .'' Proceedings of the National Academy of Sciences, Vol. 120, No. 25, e2217322120.
McCartan, Cory, Christopher T. Kenny, Tyler Simko, George Garcia III, Kevin Wang, Melissa Wu, Shiro Kuriwaki, and Kosuke Imai. (2022). ``Simulated redistricting plans for the analysis and evaluation of redistricting in the United States: 50stateSimulations.'' Scientific Data, Vol. 9, No. 689, pp. 1-10.
Fifield, Benjamin, Michael Higgins, Kosuke Imai, and Alexander Tarr. (2020). ``Automated Redistricting Simulation Using Markov Chain Monte Carlo.'' Journal of Computational and Graphical Statistics, Vol. 29, No. 4, pp. 715-728.
Fifield, Benjamin, Kosuke Imai, Jun Kawahara, and Christopher T. Kenny. (2020). ``The Essential Role of Empirical Validation in Legislative Redistricting Simulation.'' Statistics and Public Policy, Vol. 7, No. 1, pp 52-68.
McCartan, Cory, and Kosuke Imai. (2023). ``Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans.'' Annals of Applied Statistics, Vol. 17, No. 4 (December), pp. 3300-3323.
Fifield, Benjamin, Christopher T. Kenny, Cory MaCartan, and Kosuke Imai. ``redist: Computational Algorithms for Redistricting Simulation.'' available through The Comprehensive R Archive Network and GitHub.

© Kosuke Imai
 Last modified: Wed Jul 17 05:43:16 EDT 2024