Bibliographic record
Abstract
We design and field an innovative survey of unemployment insurance (UI) recipients that yields new insights about wage stickiness on the layoff margin. Most UI recipients express a willingness to accept wage cuts of 5-10 percent to save their jobs, and one-third would accept a 25 percent cut. Yet worker-employer discussions about cuts in pay, benefits, or hours in lieu of layoffs are exceedingly rare. When asked why employers don’t raise the possibility of job-preserving pay cuts, four-in-ten UI recipients don’t know. Sixteen percent say cuts would undermine morale or lead the best workers to quit, and 39 percent don’t think wage cuts would save their jobs. For those who lost union jobs, 45 percent say contractual restrictions prevent wage cuts. Among those on permanent layoff who reject our hypothetical pay cuts, half say they have better outside options, and 38 percent regard the proposed pay cut as insulting. Our results suggest that wage cuts acceptable to both worker and employer could potentially prevent a quarter of the layoffs in our sample. We draw on our findings and other evidence to assess theories of wage stickiness and its role in layoffs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".