McCartan, Cory, Christopher Kenny, Tyler Simko, Emma Ebowe, Michael Zhao, and Kosuke Imai. ``Redistricting Reforms Reduce Gerrymandering by Constraining Partisan Actors.'' American Political Science Review, Forthcoming.
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. |
Kenny, Christopher T., Brian Zhou, Tyler Simko, and Kosuke Imai
(2026).
``Weakening the Voting Rights Act reduces minority representation and electoral competition.''
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O'Sullivan, Philip, Kosuke Imai, and Cory McCartan
(2026).
``Generalized Sequential Monte Carlo Sampling for Redistricting Simulation.''
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Miyazaki, Sho, Kento Yamada, and Kosuke Imai
(2025).
``Estimating the Partisan Bias of Japanese Legislative Redistricting Plans Using a Simulation Algorithm.''
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Jasny, Ethan, Christopher T. Kenny, Cory McCartan, Tyler Simko, Melissa Wu, Michael Y. Zhao, Aneetej Arora, Emma Ebowe, Philip O'Sullivan, Taran Samarth, and Kosuke Imai
(2025).
``Gerrymandering and geographic polarization have reduced electoral competition.''
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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.
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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.
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Kenny, Christopher T., Shiro Kuriwaki, Cory McCartan, Evan Rosenman, Tyler Simko, and Kosuke Imai
(2023).
``Comment: The Essential Role of Policy Evaluation for the 2020 Census Disclosure Avoidance System.''
Harvard Data Science Review, Special Issue 2: Dierential Privacy for the 2020 U.S. Census (January).
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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.''
Scientific Data, Vol. 9, No. 689, pp. 1-10.
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Kenny, Christopher T., Shiro Kuriwaki, Cory McCartan, Evan T.R. Rosenman, Tyler Simko, and Kosuke Imai
(2021).
``The Use of Differential Privacy for Census Data and its Impact on Redistricting: The Case of the 2020 U.S. Census.''
Science Advances, Vol. 7, No. 7 (October), pp. 1-17.
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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.
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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.
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