By Francis Northwood (PO ’21)
Two new Gerrymandering cases have just been heard by the Supreme Court: Rucho v. Common Cause, a case in North Carolina, and Lamone v. Benisek in Maryland. The topic of Gerrymandering has become popular for the Supreme Court, with these two new cases appearing just a year after the court unanimously punted the topic in a different case. In that 2018 case Gill v. Whitford, the Supreme Court remanded the case to the lower courts—somewhat in favor of the redistricting Wisconsin Republican legislators. The case, brought by the Campaign Legal Center (CLC), argued that Wisconsin Democrats’ votes were being wasted because of maliciously-constructed voting districts. Their argument was constructed on a math-based concept called the efficiency gap, a measure created by University of Chicago law professor Nicholas Stephanopoulos and political scientist Eric McGhee. Stephanopoulos explains the metric concisely in a New Republic piece
“Suppose, for example, that a state has five districts with 100 voters each, and two parties, Party A and Party B. Suppose also that Party A wins four of the seats 53 to 47, and Party B wins one of them 85 to 15. Then in each of the four seats that Party A wins, it has 2 surplus votes (53 minus the 51 needed to win), and Party B has 47 lost votes. And in the lone district that Party A loses, it has 15 lost votes, and Party B has 34 surplus votes (85 minus the 51 needed to win). In sum, Party A wastes 23 votes and Party B wastes 222 votes. Subtracting one figure from the other and dividing by the 500 votes cast produces an efficiency gap of 40 percent in Party A’s favor.”
According to Stephanopoulos, an efficiency gap of near zero percent would be indicative of a state whose legislature proportionally reflects the political views of its citizens. Wisconsin had an efficiency gap favoring Republicans anywhere from 11.69% to 13%. Yet, the Supreme Court did not vote in favor of the plaintiffs. The opinion, written by then-Justice Anthony Kennedy, outlines burdens unmet by the plaintiffs. The opinion details that the Court is not concerned with the “the effect that a gerrymander has on the fortunes of political parties,” but rather “individual legal rights.” While individuals do have a right to free-association, the right that the CLC and company brought forward is a right to elect the preferred candidate. Gill v. Whitford places the burden upon future plaintiffs to show “the effect that a gerrymander has on the votes of particular citizens…” which, in turn, cannot be on the basis of their interest “in their collective representation in the legislature,” and in influencing the legislature’s overall “composition and policymaking.”
To summarize, the plaintiffs presented the problem of wasted votes in a proportional sense, in that they showed that this ‘gerrymandering’ caused a lack of proportionality on the collective level, without showing individual harm.
With new cases making their way to the Supreme Court, new amicus briefs and strategies had to be prepared in response to last year’s opinion. Further, the mathematical methodology has developed. Markov Chain Monte Carlo (or MCMC) is the new buzzy math phrase dominating the courts. Markov Chain Monte Carlo is a group of algorithmic methods for sampling data. It starts with base units—precincts or census blocks—and then looks at all the different ways to combine them. MCMC involves branching out from a district probabilistically, which only works on a large scale and when using complex math (ergodic theory, to those interested). When used properly, MCMC can create district maps that are representative of the universe in which they were created and can point out maps that are outliers—those with unnatural districts.
Outliers indicate which districts are either being “cracked” or “packed.” The latter refers to the practice of concentrating all of the opponent’s voters in one district, and the former refers to the practice of diluting the opponent’s voters over many districts. MCMC can find which districts are unnatural outliers. This ability to find gerrymandered districts is also key because it does not factor in proportionality. MCMC even shows how proportionality is not a good measure of proper redistricting. In Massachusetts, where nine congressional seats are held by Democrats despite 30% of the state being Republican, proportionality would not make much sense. Massachusetts Republicans are evenly distributed across the state—in majority Democrat regions—in such a way that MCMC methods reveal there is no possible way of redistricting the state in order to give the Republicans a single seat. This is where MCMC and the efficiency gap diverge, as the efficiency gap would say that there were problems in the state.
The final advantage of MCMC is that it allows for the consideration of states’ redistricting laws. For example, North Carolina does not want districts to split up counties, so North Carolina could use that to “adjust” its conclusions from MCMC.
The cracking and packing of districts meets the burden regarding individual harm proposed earlier. If a person were to live in a district that was irregular, their individual voting rights would be said to be damaged by the irregular nature of the district. At least, this is the case that the plaintiffs are trying to present.
Supreme Court Justices are not mathematicians, but they at least understand when something is an outlier. MCMC methods when used in North Carolina found that the state’s House seats should likely be redistricted 7-6 in favor of Republicans, a far cry from the 10-3 the state’s representatives currently sit at. With Kennedy off the Supreme Court, the cases being brought have to also retailored to convince the new swing vote, Chief Justice John Roberts—a task that may end up being easier than expected, after Kennedy called the efficiency gap “sociological gobbledygook.” Six justices—Alito and Roberts being the two conservatives—have established that they at least believe that gerrymandering is wrong. The question for them is: when does partisan gerrymandering become too much? Markov Chain Monte Carlo methods attempt to answer that with universal applications.