Estimating the Partisan Bias of Japanese Legislative Redistricting Plans Using a Simulation Algorithm

 

  Abstract

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.

  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, 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: Mon Oct 21 22:45:48 BST 2024