Georgia’s “exact match” voter registration law has the potential to disenfranchise nearly 910,000 people — disproportionately black and Hispanic voters — according to a new analysis by Ted Enamorado, a PhD candidate in the politics department at Princeton University.
> Recently, there’s been an uproar about Georgia’s approach to voter registration. The state’s “exact match” law, passed last year, requires that citizens’ names on their government-issued IDs must precisely match their names as listed on the voter rolls. If the two don’t match, additional verification by a local registrar will benecessary. The Georgia NAACP and other civil rights groups have filed a lawsuit arguing that the measure, effective since July 2017, is aimed at disenfranchising racial minorities in the upcoming midterm elections.
> Georgia Secretary of State Brian Kemp, a Republican who is running for governor against Democrat Stacey Abrams, has put on hold more than 53,000 voters so far, given mismatches in the names in their voting records and other sources of identification such as driver’s licenses and Social Security cards. If the measure takes effect, voters whose information does not exactly match across sources will need to bring a valid photo ID to the polls on Election Day to vote. That could suppress voter turnout, either because some voters lack IDs or because voters are confused about whether they are eligible. Proponents of the rule assert that it is only meant to prevent illegal voting.
But Enamorado questioned whether minor discrepancies between identification sources are necessarily a good indicator that records do not match — and he should know.
He has been working on an algorithm that can accomplish linking data sets with high probability that they match.
> In doing empirical scientific research, they often need to link various sets of data by some imperfect identifier — say, agency names or individual addresses. While doing this can be tedious, getting the matches correct is crucial. Match the wrong records, and any analysis may be totally unreliable. That leads many data analysts to only retain exact matches.
> But although incorrect matches can cause problems, so can dropping records that should be matched but have small discrepancies. Eliminating those records can also corrupt an analysis.
> That’s why I have spent the past three years helping to develop an algorithm that uses probabilistic record linkage called “fastLink” that not only makes record linkage across data sets speedy and automated, but also tells the analyst how likely it is that an inexact match of two records is actually correct.
Together with colleagues Ben Fifield and Kosuke Imai, Enamorado applied the algorithm to the issue of voter identification reported their findings in The Post.
Their conclusion? As many as 909,540 Georgians could be prevented from casting ballots using the “exact match” method, if no further checks are conducted.
Enamorado describes their process:
> We worked on linking two nationwide voter files from 2014 and 2015 collected by L2 Inc, a national nonpartisan firm that supplies voter data and related technology for campaigns. All active voters in 2014 appeared in the 2015 data set — meaning that we knew a true match always existed. But many records had typographical discrepancies preventing exact matches.
> Our analysis found that the “exact match’’ approach would link only 66 percent of voters who were actually the same, correctly identifying about 91 million voters. In other words, “exact matching” would exclude nearly 40 million records that actually did refer to the same voter — disenfranchising quite a few Americans.
As for Georgia?
> Georgia’s records had a higher proportion of exact matches than we found nationwide — but 30 percent of actual voters still failed to exactly match in that state.
> By contrast, using our algorithm, which correlates with L2’s in-house matching records nearly perfectly (r=.99), we are able to match almost 127 million registered voters — or 93 percent of all voters in the 2014 data. Among those whose records did not exactly match, we found that 25 percent have at least a 99 percent probability of being correct matches, while 28 percent have at least a 95 percent probability.
> Using our algorithm, in other words, 91 percent of those on Georgia’s voter rolls would be cleared to vote, or 3,941,342 voting-eligible citizens — while “exact matching” clears only 70 percent, with the potential of disenfranchising 909,540 eligible citizens if no additional checks are conducted.
> And in keeping with the concerns of opponents of the Georgia measure, nonwhite voters are especially likely to be harmed. The match rates using exact matching are nine and six percentage points lower for black and Hispanic voters, respectively, than for white voters.