In ADGN: An Algorithm for Record Linkage Using Address, Date of Birth, Gender, and Name, newly published in Statistics and Public Policy, a pair of researchers from Harvard and Tufts build a statistical model to analyze the impact of the voter ID laws passed in Republican-controlled states as part of a wider voter suppression project that was explicitly aimed at suppressing the votes of racialised people, historically likely to vote Democrat.
There have been numerous profiles of the people targeted by these laws (the NYT even made an Oregon-Trail-style game based on the kafka-esque rigamarole the GOP put in the way of poor and black voters).
Supreme Court justice dismissed these first-person reports as "sociological gobbledygook," so the researchers set out to create an unimpeachable, quantitative, empirical model showing that voter ID laws disproportionately disenfranchised black and brown people, especially poor ones.
That's what they found.
Once Hersh and Ansolabehere were confident they had properly matched registered voters to their ID records, they used a commercial tool called Catalist to predict each voter's race. That tool analyzes names to determine how likely a given name is to be associated with one race or another. It also accounts for the demographics of the Census block where a given voter lives. Using this tool, the researchers confirmed what voting rights advocates already know to be true—that black voters are more likely to lack adequate identification under voter ID laws. According to the study, 3.6 percent of registered white voters had no match in any state or federal ID database. By contrast, 7.5 percent of black registered voters were missing from those databases.
The algorithm shows a clear and disturbing racial disparity on voting rights. But Hersh says that it also shows that voter ID laws affect a relatively small percentage of the population. Across all registered voters in Texas, the researchers found 4.5 percent lack proper identification. For registered voters who actually showed up at the polls in 2012, it's 1.5 percent.
ADGN: An Algorithm for Record Linkage Using Address, Date of Birth, Gender, and Name [Stephen Ansolabehere and Eitan Hersh/Statistics and Public Policy]
A Dead-Simple Algorithm Reveals the True Toll of Voter ID Laws [Issie Lapowsky/Wired]
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