Disincentive Effects of Unemployment Insurance Benefits
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
Unemployment insurance (UI) acts both as a disincentive for labor supply and as a demand stimulus which may explain why empirical studies often find limited effects of UI on employment. This paper provides independent estimates of the disincentive effects arising from the largest expansion of UI in U.S. history, the pandemic unemployment benefits. Using high-frequency data on small restaurants and retailers from Homebase, we control for local demand effects by comparing neighboring businesses that largely share the positive impact of UI stimulus. We find that employment in low-wage businesses recovered more slowly than employment in high-wage businesses in labor markets with larger differences in the relative generosity of pandemic UI benefits. According to a labor search model that replicates the estimated employment differences between low-and high-wage businesses, the disincentive effects from the pandemic UI programs held back the aggregate employment recovery by 4.7 percentage points between April and December 2020.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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