Lessons from the Use of Ranked Choice Voting in American Presidential Primaries

Open Access Journal | ISSN: 2183-2463

Article | Open Access

Lessons from the Use of Ranked Choice Voting in American Presidential Primaries


  • Rob Richie FairVote, USA
  • Benjamin Oestericher FairVote, USA
  • Deb Otis FairVote, USA
  • Jeremy Seitz-Brown FairVote, USA


Full Text   PDF (free download)
Views: 1377 | Downloads: 371


Abstract:  Grounded in experience in 2020, both major political parties have reasons to expand use of ranked choice voting (RCV) in their 2024 presidential primaries. RCV may offer a ‘win-win’ solution benefiting both the parties and their voters. RCV would build on both the pre-1968 American tradition of parties determining a coalitional presidential nominee through multiple ballots at party conventions and the modern practice of allowing voters to effectively choose their nominees in primaries. Increasingly used by parties around the world in picking their leaders, RCV may allow voters to crowd-source a coalitional nominee. Most published research about RCV focuses on state and local elections. In contrast, this article analyzes the impact on voters, candidates, and parties from five state Democratic parties using RCV in party-run presidential nomination contests in 2020. First, it uses polls and results to examine how more widespread use of RCV might have affected the trajectory of contests for the 2016 Republican nomination. Second, it contrasts how more than three million voters in the 2020 Democratic presidential primaries backed withdrawn candidates with the low rate of such wasted votes for withdrawn candidates in the states with RCV ballots. Finally, it concludes with an examination of how RCV might best interact with the parties’ current rules and potential changes to those rules.

Keywords:  electoral reform; instant runoff; presidential primaries; ranked choice voting

Published:   15 June 2021


DOI: https://doi.org/10.17645/pag.v9i2.3960


© Rob Richie, Benjamin Oestericher, Deb Otis, Jeremy Seitz-Brown. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0), which permits any use, distribution, and reproduction of the work without further permission provided the original author(s) and source are credited.