By Erica Klarreich
Partisan gerrymandering—the practice of drawing voting districts to give one political party an unfair edge—is one of the few political issues that voters of all stripes find common cause in condemning. Voters should choose their elected officials, the thinking goes, rather than elected officials choosing their voters. The Supreme Court agrees, at least in theory: In 1986 it ruled that partisan gerrymandering, if extreme enough, is unconstitutional.
“If you’re never going to declare a partisan gerrymander, what is it that’s unconstitutional?” said Wendy K. Tam Cho, a political scientist and statistician at the University of Illinois, Urbana-Champaign.
The problem is that there is no such thing as a perfect map—every map will have some partisan effect. So how much is too much? In 2004, in a ruling that rejected nearly every available test for partisan gerrymandering, the Supreme Court called this an “unanswerable question.” Meanwhile, as the court wrestles with this issue, maps are growing increasingly biased, many experts say.
Even so, the current moment is perhaps the most auspicious one in decades for reining in partisan gerrymandering. New quantitative approaches—measures of how biased a map is, and algorithms that can create millions of alternative maps—could help set a concrete standard for how much gerrymandering is too much.
Last November, some of these new approaches helped convince a United States district court to invalidate the Wisconsin state assembly district map—the first time in more than 30 years that any federal court has struck down a map for being unconstitutionally partisan. That case is now bound for the Supreme Court.
“Will the Supreme Court say, ‘Here is a fairness standard that we’re willing to stand by?’” Cho said. “If it does, that’s a big statement by the court.”
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