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Who Votes Without Identification? Using Individual‐Level Administrative Data to Measure the Burden of Strict Voter Identification Laws

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  • Phoebe Henninger
  • Marc Meredith
  • Michael Morse

Abstract

Legal disputes over laws that require certain forms of identification (ID) to vote mostly focus on the burden placed on people who do not possess ID. We contend that this singular focus ignores the burden imposed on people who do possess ID, but nonetheless cannot access it when voting. To measure this alternative conception of burden, we focus on Michigan, which allows anyone who lacks access to ID to vote after signing an affidavit. A sample of affidavits filed in the 2016 presidential election from a random set of precincts reveals that about 0.45 percent of voters lacked access to ID. Consistent with our broader conception of the burden of voter ID laws, nearly all voters who filed an affidavit were previously issued a still‐active state ID. Importantly, we show minority voters were about five times more likely to lack access to ID than white voters. We also present survey evidence suggesting that people who live in states where voters are asked to show ID, as in Michigan, are more likely to incorrectly believe that access to ID is required to vote than are people who live in states that do not ask voters to show ID.

Suggested Citation

  • Phoebe Henninger & Marc Meredith & Michael Morse, 2021. "Who Votes Without Identification? Using Individual‐Level Administrative Data to Measure the Burden of Strict Voter Identification Laws," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 18(2), pages 256-286, June.
  • Handle: RePEc:wly:empleg:v:18:y:2021:i:2:p:256-286
    DOI: 10.1111/jels.12283
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    References listed on IDEAS

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    1. Enrico Cantoni & Vincent Pons, 2021. "Strict Id Laws Don’t Stop Voters: Evidence from a U.S. Nationwide Panel, 2008–2018," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(4), pages 2615-2660.
    2. Imai, Kosuke & Khanna, Kabir, 2016. "Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records," Political Analysis, Cambridge University Press, vol. 24(2), pages 263-272, April.
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