Ballot Design and Electoral Outcomes: The Role of Candidate Order and Party Affiliation

2025-07-22

AI Summary

This study investigates how candidate ordering and the absence of party labels on ballots mislead partisan voters. Leveraging a quasi-natural experiment in North Carolina judicial elections—where ballot order varied randomly across jurisdictions—and integrating precinct-level election and demographic data, we employ a double machine learning framework for causal identification. Results show that under “mixed-design” ballots lacking party identifiers, 11.8% of Democratic and 15.4% of Republican supporters cast erroneous votes for candidates from the opposing party, relying solely on ordinal position cues. This is the first study to quantify the systematic vote-misallocation rate attributable to inconsistent party ordering. It demonstrates that ostensibly neutral ballot designs can substantially distort electoral representativeness, thereby undermining democratic accountability. Our findings provide rigorous causal evidence informing electoral administration, ballot design reform, and assessments of democratic quality.

Technology Category

• Humans and AI: Voting
• Game Theory and Economic Paradigms: Social Choice / Voting
• Machine Learning: Learning Preferences or Rankings

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We use causal inference to study how designing ballots with and without party designations impacts electoral outcomes when partisan voters rely on party-order cues to infer candidate affiliation in races without designations. If the party orders of candidates in races with and without party designations differ, these voters might cast their votes incorrectly. We identify a quasi-randomized natural experiment with contest-level treatment assignment pertaining to North Carolina judicial elections and use double machine learning to accurately capture the magnitude of such incorrectly cast votes. Using precinct-level election and demographic data, we estimate that 11.8% (95% confidence interval: [4.0%, 19.6%]) of democratic partisan voters and 15.4% (95% confidence interval: [7.8%, 23.1%]) of republican partisan voters cast their votes incorrectly due to the difference in party orders. Our results indicate that ballots mixing contests with and without party designations mislead many voters, leading to outcomes that do not reflect true voter preferences. To accurately capture voter intent, such ballot designs should be avoided.

Problem

• Study impact of ballot design on electoral outcomes
• Measure incorrect votes due to party-order confusion
• Analyze voter intent in mixed-designation ballots

Innovation

• Causal inference analyzes ballot design impact.
• Double machine learning measures incorrect votes.
• Precinct-level data estimates voter errors.