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Despite intense gerrymandering by both parties in individual states, the net effect at the national level has created a balanced map. A 50% national popular vote for a party is now predicted to yield 50% of the seats, an unusual level of fairness by recent historical standards.

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Despite the vitriol on social media and in political discourse, the actual social reality is not nearly as polarized. On fundamental issues like the fairness of gerrymandering or the need for a welfare system, there is massive agreement between Democrats and Republicans. Political actors and media amplify conflict, creating a participatory 'cosplay' of division that obscures vast common ground.

A common assumption is that a neutral process is inherently fair. However, due to natural population clustering (e.g., Democrats in cities), a randomly drawn map can still heavily favor one party. Achieving fairness may require intentional design to counteract geographic disadvantages, not just the absence of malicious intent.

Politicians are fundamentally incapable of drawing fair electoral boundaries due to an inherent conflict of interest: they want to ensure their party wins. Using a randomly sampled citizens' commission, as Michigan did, removes this conflict. This allows ordinary people, guided by a sense of fairness, to create equitable maps where politicians and courts have failed.

A common focus in redistricting reform is preventing 'crazy-looking' districts. However, this is a red herring. A legislature can easily create visually compact, 'nice-looking' districts that are just as politically skewed, making district shape an unreliable metric for fairness.

In gerrymandered districts, the primary election, not the general, is the real contest. This system empowers the most extreme voters who dominate primaries, leading to the election of highly polarized officials who are unwilling to compromise, creating legislative gridlock and fueling political division.

To analyze a proposed map's fairness, mathematicians compare it to a representative sample of alternatives. They use a Markov chain—a 'random walk' making sequential changes to a map—to explore the astronomically large space of possibilities without enumerating it, creating a baseline for what 'typical' maps look like.

The combinatorial complexity of drawing district maps is vastly underestimated, even by Supreme Court justices. The number of possibilities isn't in the thousands but is astronomically large (like a googol), making it impossible to check every option and thus requiring sophisticated mathematical sampling techniques.

With over 90% of congressional districts being non-competitive, the primary election is often the only one that matters. Buttigieg argues this incentivizes candidates to appeal only to their party's extreme flank, with no need to build broader consensus for a general election.

Gerrymandering and political sorting have created effective one-party states (like California and Texas). As a result, meaningful political choice is no longer about flipping your state's politics, but about physically moving to a state that already aligns with your values. The most powerful vote is cast with a moving truck.

Instead of single-winner districts, a powerful reform is creating larger, multi-member districts that elect several representatives (e.g., 4 districts electing 3 members each). This allows for more proportional outcomes that reflect an area's political diversity, as a minority group can win one of the multiple seats.

Partisan Gerrymandering Inadvertently Created a 'Perfectly Fair' U.S. National House Map | RiffOn