Perceived Weight and Election Outcomes for 3,612 U.S. Congressional Candidates in 2022

3,612 candidate photographs from the 2022 U.S. Congressional Elections were rated for perceived weight by an online sample of 669 participants. Gothreau, Claire matched these perceptions with real-world election outcomes to measure weight-based discrimination, finding an electoral penalty for obesity roughly 50% as large as lacking a college degree.

Use Cases

• Quantifying electoral penalties based on perceived candidate weight from image ratings.
• Analyzing discrimination patterns across political parties and candidate genders.
• Investigating the impact of candidate novelty (new entrants) on weight-based bias.
• Comparing weight-based discrimination to other demographic penalties like education level.

Strengths

• Includes data for 3,612 candidates, providing a substantial sample for analysis.
• Links perceptual data from 669 raters with real election outcomes.
• Analysis reveals a specific, quantified penalty: obesity carries an electoral penalty roughly 50% as large as lacking a college degree.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the final matched dataset is unknown, which may limit suitability assessment.

Provenance

Source

The Journal of Politics Dataverse

Collection Method

Candidate photographs were scraped and then rated for demographics, including weight, by an incentivized online sample; perceptions were matched with election outcomes.

Time Range

2022 U.S. Congressional Elections

Freshness

Last updated 2026-06-05 22:22:11; freshness should be verified.

Geography

United States