All pain, whose gain? The surprising implications of a new legal theory for redistricting
(cross-posted with my new Substack) Lots of pixels have been spilled on a legal theory once considered fringe, the Independent State Legislatu...
Senate: 49 Dem | 51 Rep (range: 47-52)
Control: (R+1.8%) from toss-up
Generic polling: D+2.0%
Control: D+2.0%
Biden/Harris 267 EV (R+0.1%)
Moneyball states: President CA FL TX
Anyone interested in containing partisan gerrymanders is waiting for several major decisions from the Supreme Court this month. But no matter which way those decisions go, the next stage of reform will be local. For this reason, my team at the Princeton Gerrymandering Project is making plans to expand our research efforts, which bridge mathematics and the law, to individual states.
Here’s what we have planned ahead in our effort to fix a major bug in democracy.
Our Work
The Princeton Gerrymandering Project seeks to bridge the gap between mathematics and the law to achieve fair representation through redistricting reform. This work is nonpartisan. Previously, we developed standards for detecting partisan inequality of opportunity and outcome (see this Stanford Law Review article and this Harvard Law Review Blog post). These standards offer one way to put guardrails on the redistricting process.
Adding A Dose of Federalism
A potentially far stronger route to reform goes through individual states – a federalist approach. Our Project has now expanded to include map-drawing, computational, and legal expertise. This interdisciplinary team aims to give activists and legislators the tools they need to detect offenses and craft bombproof, bipartisan reform.
Our statistics have been used to demonstrate just how unfair a gerrymander can be. Our geospatial analyst has experience in drawing demonstrative maps that have been used by state reformers to identify extreme gerrymanders and neutral alternatives. And working with legal collaborators, we have filed a brief to turn the math into a standard of fairness that can be expressed in terms of law.
The Future
In the future, the Princeton Gerrymandering Project seeks to achieve:
We’ll say more about our work in the weeks and months ahead. I hope you can support our work to fix bugs in democracy.
Fair Districts PA is working to reform the redistricting process in PA. Thank you for your work. https://www.fairdistrictspa.com
Voters Not Politicians is a grassroots, citizen-led initiative working to end gerrymandering in Michigan. The campaign collected more than 425,000 signatures with all volunteers last fall and is launching a robust field and communications strategy leading up to the election to get a majority yes vote on the ballot measure to amend Michigan’s Constitution and create an Independent Citizens Redistricting Commission!
Too late for 2018, but here is my suggestion for a geometric standard to minimize gerrymandering:
The distance of the longest line that can be drawn between the center of area of a district and its perimeter divided by the shortest line be no greater than a value (something like 3). (This alone would eliminate the worst examples of gerrymandering.)
This, of course, could be combined with other geometric requirements, such as a ratio between area and perimeter distance, and/or percentage of area within an encompassing circle.
Right now there are many anti-gerrymandering amendments on bills around the country. But they involve “independent” people, which of course can be corrupted. I fear the amendments may either be tossed by a judge down the road, or if corruption is found, the redrawn districts may be invalidated. So it seems to me that some sort of geometric basis would have fewer risks. And, of course, geometric standards need not be exclusive from the current or proposed methods of drawing lines.
Additional geometric standards would be a step in the wrong direction. Such a constraint ties redistricters’ hands for VRA compliance and other positive goals, and doesn’t actually prevent creative offenses.
Generally, algorithmic approaches are a dead end for addressing real-life representational problems. They fail to take into account the needs of real communities.