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Published November 2020 | public
Book Metadata-only

Securing American Elections: How Data-Driven Election Monitoring Can Improve Our Democracy

Alvarez, R. Michael ORCID icon
Adams-Cohen, Nicholas ORCID icon
Kim, Seo-young Silvia ORCID icon
Li, Yimeng ORCID icon
Others:
Alvarez, R. Michael
Bacchus, Emily Beaulieu
Stewart, Charles, III

Abstract

The integrity of democratic elections, both in the United States and abroad, is an important problem. In this Element, we present a data-driven approach that evaluates the performance of the administration of a democratic election, before, during, and after Election Day. We show that this data-driven method can help to improve confidence in the integrity of American elections.

Additional Information

© 2020 R. Michael Alvarez, Nicholas Adams-Cohen, Seo-young Silvia Kim, and Yimeng Li. Online publication date: November 2020. A project like this cannot be successfully conducted without a lot of advice, input, and assistance. First of all, we thank Neal Kelley, the Orange County Registrar of Voters, for his collaboration with us on this project. Neal's advice, enthusiasm, and interest in using data to improve the election process in Orange County provided the foundation for our project. Working with academic researchers is not easy, and Neal's willingness to collaborate with us, his patience with our requests, and his comments and critiques of our work, were important for our success. We also thank Justin Berardino of the OCROV for his help – Justin, the OCROV's Operations Manager, played a crucial role in helping us with data and information about elections in Orange County. Second, we thank the John Randolph Haynes and Dora Haynes Foundation, which provided funding for this project. The mission of the Haynes Foundation is to support social science research, especially in Southern California. They have long provided financial support for research that seeks to improve a social scientific understanding of California's unique democracy, and we hope that our research reported in this Element contributes to that same understanding. Third, a number of people have helped us with this research project. Sabrina Hameister at Caltech assisted with project logistics, and we could not have easily run this project without her help. A number of Caltech students participated in this research project, and we thank them for their assistance: Jack Briones, Ethan Eason, Daniel Guth, Claire Ho, Joanna Huey, Michelle Hyun, Cheria Jia, Nailen Matschke, Matt Riker, and Spencer Schneider. Academic colleagues provided comments and advice at various stages in this project, and we thank Lonna Atkeson, Paul Gronke, Thad Hall, Jonathan Katz, Ines Levin, Paul Manson, and Charles Stewart for their support and advice. Some of the research reported here has been presented at research conferences and workshops, especially our work on voter registration database auditing: We thank participants at the 2018 Southern California Methods Conference (especially Steven Liao), and Election Audit Summit held at MIT, December 7–8, 2018. The registration auditing project was also presented at the 2019 Annual Meeting of the Midwest Political Science Association, the Election Sciences, Reform, and Administration 2019 conference, and the 2019 Annual Meeting of the Society for Political Methodology – we thank participants from those conferences for their comments and questions. We presented some of the material from this project at the "Election Administration and Technology Symposium," hosted by the Bedrosian Center at the USC Sol Price School of Public Policy; we thank Jeff Jenkins for inviting us, and for hosting the symposium, and of course we thank the symposium participants for their comments on our work. Finally, code and data from our research are available on our project's GitHub (https://github.com/monitoringtheelection). Due to the ongoing nature of this project, and the sensitive nature of some of the data, only certain datasets and code will be available on the GitHub. Researchers who are interested in data or code that is not on the project GitHub are encouraged to contact the authors.

Additional details

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Created:
August 20, 2023
Modified:
January 15, 2024