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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., 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.
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