In 2011, the Texas state legislature passed a bill requiring that residents present certain types of identification before being allowed to vote. The U.S. Department of Justice filed a lawsuit against Texas, arguing that the intent and effect of the bill was to discriminate against minority voters. That’s where Eitan Hersh, an associate professor of political science at Tufts, came in. Working as a consultant for the Department of Justice, along with a colleague at Harvard, Hersh devised a way to determine who qualified to vote under the controversial law, known as S.B. 12. Using an algorithm, and delving into millions of publicly available records, he determined that while fewer registered voters lacked the necessary ID than had been thought, the effect of the law was clearly discriminatory, disproportionately affecting minorities. To qualify to vote under the law, registered voters had to present a state driver’s license or ID card, a concealed handgun license, a U.S. passport, a military ID card, or a U.S. citizenship certificate with a photo.
Now Hersh has described his methodology in the journal Statistics and Public Policy. In the paper, he and Stephen Ansolabehere, a professor of government at Harvard, show how they matched people listed on the Texas election voter files with residents having one of the acceptable forms of ID by using only address, date of birth, gender, and name data. The resulting data were almost as good as matching Social Security numbers. The researchers also classified the voters as either Anglo, black, Hispanic, or “other races”, to determine if there were discriminatory effects of the law.
“In the last decade, states have been changing rules about registration, early voting, and voter ID,” said Hersh. “Voter ID is particularly controversial, because some of these laws seem to have been passed into law with a discriminatory intent.”
Full Article: Algorithm proves voter ID law’s discriminating intent.