Expressive Responding and the Economy: The Case of Trump’s Return to Office
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The partisan gap in economic perceptions flipped unusually dramatically after the 2024 U.S. presidential election: following the Republican victory, Democrats (Republicans) suddenly rated the economy much more negatively (positively). Was the resulting partisan difference a case of expressive responding, wherein surveys exaggerate partisan bias in measures of economic perceptions? In April 2025, I fielded a panel survey experiment that asked survey respondents to guess then-unpublished measures of economic growth, inflation, and unemployment in the current month or quarter (Prolific, N = 2,831). Randomly selected respondents were offered $2 per correct answer. Partisan bias did not shrink as a result, suggesting genuine differences in economic perceptions. Two measures of response effort (response time and looking up answers) increase, suggesting that misreporting does not fully explain the effects of pay-for-correct treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it