Princeton Election Consortium

Innovations in democracy since 2004

Outcome: Biden 306 EV (D+1.2% from toss-up), Senate 50 D (D+1.0%)
Nov 3 polls: Biden 342 EV (D+5.3%), Senate 50-55 D (D+3.9%), House control D+4.6%
Moneyball states: President AZ NE-2 NV, Senate MT ME AK, Legislatures KS TX NC

A statistical toolbox reveals Pennsylvania’s extreme Congressional plan

February 13th, 2018, 8:27am by Sam Wang

Today, highlights multiple measures of partisan gerrymandering, including several developed here at Princeton.

Now, a major disclaimer: I didn’t think of these, exactly. Several tests (mean-median difference, and lopsided-wins) are over a hundred years old. Another (simulated elections) relies on an equally-old technique, Monte Carlo simulations. These tests are so old that they have whiskers.

There’s one lesson, though: there isn’t just one way to evaluate a gerrymander. Think of these as tools that capture different aspects of partisan asymmetry. For example, Monte Carlo simulations actually account for some of the clustering effect that comes from Republicans gravitating toward rural areas and Democrats gravitating toward population centers.

For more on the tests, see our explainer. If you like, support our work!

Tags: 2018 Election · Redistricting

2 Comments so far ↓

  • LondonYoung

    On tools for evaluating Gerrymandering, nobody starts from scratch.
    Isaac Newton in 1675: “If I have seen further it is by standing on the shoulders of Giants.”

  • TokyoStreetView

    LondonYoung, thank you for this post. Its very inspiring.

Leave a Comment