``Gerrymandering and geographic polarization have reduced electoral competition.''

Changes in political geography and electoral district boundaries shape representation in the United States Congress. To disentangle the effects of geography and gerrymandering, we generate a large ensemble of alternative redistricting plans that follow each state's legal criteria. Comparing enacted plans to these simulations reveals partisan bias, while changes in the simulated plans over time identify shifts in political geography. Our analysis shows that geographic polarization has intensified between 2010 and 2020: Republicans improved their standing in rural and rural-suburban areas, while Democrats further gained in urban districts. These shifts offset nationally, reducing the Republican geographic advantage from 14 to 10 seats. Additionally, pro-Democratic gerrymandering in 2020 counteracted earlier Republican efforts, reducing the GOP redistricting advantage by two seats. In total, the pro-Republican bias declined from 16 to 10 seats. Crucially, shifts in political geography and gerrymandering reduced the number of highly competitive districts by over 25%, with geographic polarization driving most of the decline.

McCartan, Cory, Christopher Kenny, Tyler Simko, Emma Ebowe, Michael Zhao, and Kosuke Imai. ``Redistricting Reforms Reduce Gerrymandering by Constraining Partisan Actors.''

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.'' Scientific Data, Vol. 9, No. 689, pp. 1-10.

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.