The Differential Effects of Strict Voter Identification Laws on Election Type and Adoption Timing
Rivka studied U.S. voter ID laws with statistical modeling. Using voter turnout data from 1984 to 2020, she compared states with strict voter ID laws to those without.
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Rivka Lipkovitz, 18, of San Francisco, used statistical modeling to study U.S. voter ID laws for her Regeneron Science Talent Search social sciences project. Some believe these laws prevent fraud; others say they stop people from voting. Research on their effects on voter turnout has mixed results.
For her project, Rivka analyzed voter turnout data between 1984 and 2020. She compared states with strict voter ID laws to those without. Rivka used statistical methods called matrix completion and synthetic difference-in-differences. She created a counterfactual estimate of voter turnout if states had not passed voter ID laws. States that passed strict laws after 2008 had a 2.4% drop in presidential election turnout. States that passed laws before or during 2008 had no change in turnout. In midterm elections, voter turnout appeared to increase. She believes her findings can help policymakers decide whether to pass or change voter ID laws.
Rivka, the daughter of Eisar and Uyen Lipkovitz, attends Proof School. Rivka is fascinated by methods to “mathematically explore alternative paths of history” and hopes to work at the Federal Reserve.
Beyond the Project
Rivka has a blog where she shares insights on performance trends in competitive fencing earning her the nickname “the fencing statistician.” She was invited to present her findings at the University of Connecticut Sports Analytics Symposium.
FUN FACTS: Rivka enjoys speedcubing competitions. She is among the top 200 women in the United States and can solve a 3×3 cube in less than 10 seconds.
