The South Carolina Racial Gerrymandering Case

Earlier this week, the Supreme Court agreed to hear a racial gerrymandering case involving South Carolina’s First Congressional District. At first glance, the issue presented in the case is fairly conventional: Did race or partisan advantage predominantly explain the First District’s design? This is the same race-or-party question the Court first confronted in Easley v. Cromartie in 2001 and also addressed in Cooper v. Harris in 2017. But there are a couple interesting wrinkles in the South Carolina case that make it more than a rerun of Easley and Cooper.

First, the First District is a “stripped” rather than a “packed” district—a district whose African American population is allegedly artificially low because of the race-based removal of Black voters. The Supreme Court has never previously considered a racial gerrymandering challenge to a stripped district. (Though lower courts have—e.g., in this successful suit against both packed and stripped Jacksonville city council districts.) Second, the plaintiffs in the South Carolina case relied in part on sets of randomly generated race-blind district maps. These maps demonstrated that, without race as a factor, the First District essentially never had as low of a Black population as it did in the enacted plan. These sorts of computer simulations have featured prominently in partisan gerrymandering cases. They’re also at the core of Alabama’s argument in Allen v. Milligan that a “race-blind baseline” should be used in cases under Section 2 of the Voting Rights Act. But until now, computer-created comparator maps haven’t appeared in any racial gerrymandering case before the Supreme Court. (Though, again, they have shown up in lower-court racial gerrymandering cases, such as the Jacksonville litigation.)

Computer simulations are especially useful in this context because the underlying issue is one of intent: Did race or party predominate in the construction of a given district? If the challenged district’s racial composition is different from that of most or all corresponding districts in the simulations, that’s powerful evidence that race predominated. If race hadn’t predominated, the challenged district’s racial composition wouldn’t be so different from that of its analogues in the simulations. Simulations also satisfy the “alternative map” requirement that the Supreme Court has inconsistently imposed on racial gerrymandering plaintiffs. This is a requirement that plaintiffs show that another map could have been drawn without race predominating and complying with all of a jurisdiction’s non-racial criteria. All of the maps produced by properly conducted simulations are such maps. So simulations yield not just one but thousands of valid alternative maps.

In contrast, simulations make much less sense in the Section 2 context. That’s because Section 2 is all about effect: whether the electoral influence of a minority group has, in fact, been diluted. Because intent is irrelevant in a typical racial vote dilution case, it’s illogical to try to establish it by comparing the enacted plan to comparator sets of randomly generated race-blind maps. True, a race-blind baseline could be used not just to shed light on intent but also to measure (a certain conception of) effect. But decades of case law make clear that the benchmark for Section 2 is race-conscious, not race-blind: a reasonable alternative map drawn with consideration—not ignorance—of race. So again, there’s no role for race-blind simulations in this kind of litigation.

At the Allen oral argument back in October, several Justices seemed to grasp these points about Section 2. Having the South Carolina racial gerrymandering case on their docket, too, may help them to see that simulations are valuable tools in some but not all suits about race and redistricting.

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