Changes in subjective well-being predict changes in U.S. presidential, Senate, & House of Representatives election outcomes
Bibliographic record
Abstract
Free and fair elections enable the nation’s citizens to elect candidates whom they believe best represent their interests. When deciding who to vote for, individuals may consider a host of factors that ultimately improve their subjective well-being. Using data from the Gallup Sharecare Well-being Index (N = 3,208,924), we examined whether changes in subjective well-being predicted U.S. presidential, Senate, and House of Representatives election outcomes from 2010 to 2020. We tested this effect at county (n = 1,652–3,061), metropolitan statistical area (n = 191–363), state (n = 50), and district (n = 389–427) levels. Pre-registered multilevel models supported the notion that regions with growing discontent tended to have larger increases in non-incumbent vote shares. Establishing a link between subjective well-being and electoral outcomes has the potential to realign policymakers’ priorities with what truly matters to their constituents, thereby facilitating the promotion of population well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".