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March 27, 2026Organization Science2 citations

VRscores: A New Measure and Data Set of Workforce Politics Using Voter Registrations

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MKMax KaganJFJustin FrakeRHReuben Hurst

Key Points

  • The study aims to create a new metric of employee partisanship by linking voter registrations to worker profiles.
  • Developed VRscores using U.S. voter registration data and matched worker profiles from 2012 to 2024.
  • Compiled a dataset covering 24.5 million workers across over 534,000 employers.
  • Compared VRscores to traditional donation-based measures of political ideology.
  • VRscores encompass a wider range of employees and organizations compared to donation-based methods.
  • The measure is more representative in terms of partisanship, seniority, occupation, and industry.
  • VRscores and donation-based measures show only a moderate correlation, classifying organizations differently in one out of five cases.

Abstract

This paper introduces VRscores, a workplace-level measure of employee partisanship constructed by linking U.S. voter registrations to electronically available worker profiles covering 2012 to 2024. The resulting organizational-level data set captures the partisanship of 24.5 million workers across more than 534,000 employers with at least five matched employees. We release this employer-level data set along with parallel data sets reporting VRscores at the firm, occupation, industry, and metropolitan statistical area levels. We show that VRscores cover substantially more employees and organizations than donation-based approaches to measuring political ideology. We also show that VRscores are more representative of the U.S. workforce in terms of partisanship, seniority, occupation, and industry. Finally, we demonstrate that VRscores and donation-based measures are only moderately correlated (Formula: see text) and that they classify one in five organizations differently with respect to whether they lean Democratic or Republican. Funding: We acknowledge generous financial support from the University of Maryland Smith School of Business, the Michigan Ross School of Business, and J. Frake acknowledges funding from the University of Michigan’s Year of Democracy Research Grant. M. Kagan acknowledges funding from the National Science Foundation Graduate Research Fellowship Program (GRFP) Grant 2146752 and the National Science Foundation Learning the Earth with Artificial Intelligence and Physics (LEAP) Science and Technology Center (STC) Grant 2019625 and the Institute for Humane Studies Grant 019144. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2025.20402 .

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Cite This Study

Kagan et al. (2026) studied this question.

synapsesocial.com/papers/69c6210b15a0a509bde19920https://doi.org/10.1287/orsc.2025.20402
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