Miyazaki, Sho, Kento Yamada, and Kosuke Imai. (2025). ``Estimating the Partisan Bias of Japanese Legislative Redistricting Plans Using a Simulation Algorithm.''
While partisan gerrymandering has been found to be widespread for Congressional redistricting in the United States, there exists little empirical research on legislative redistricting in other countries. We investigate the partisan bias of Japanese redistricting. Some scholars have argued that the prominent role played by the non-partisan commission leaves little room for partisan gerrymandering. Others have pointed out, however, that the Japanese redistricting process may be subject to political influence. The members of the redistricting commission must be appointed by the Prime Minister and be approved by the Diet. In addition, the commission invites the governors of all prefectures to provide their opinions regarding districting. We conduct a systematic empirical analysis to estimate the partisan bias of the 2022 Japanese Lower House redistricting plans. We apply a state-of-the-art redistricting simulation algorithm to generate a large number of alternative non-partisan redistricting plans. The sampled plans are representative of the population of plans and are consistent with the redistricting rules with which the commission must comply. By comparing the enacted plan with this non-partisan baseline, we quantify the extent to which the enacted plan favors a particular party. Unlike the traditional methods, our simulation approach accounts for political geography and redistricting rules specific to each prefecture. Our analysis shows that the Japanese redistricting process yields little partisan bias both at the prefecture and district levels. |
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.
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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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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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