Data and Code for: Labor Market Responses to Unemployment Insurance: The Role of Heterogeneity
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
We document considerable scope of heterogeneity within the unemployed, especially when the unemployed are divided along eligibility and receipt of unemployment insurance (UI). We study the implications of this heterogeneity on UI’s insurance-incentive trade-off using a heterogeneous-agent job-search model capable of matching the wealth and income differences that distinguish UI recipients from non-recipients. Insurance benefits are larger for UI recipients who are predominantly wealth-poor. Meanwhile, incentive costs are non-monotonic in wealth because the poorest individuals, who value employment, exhibit weak responses. Differential elasticities imply that accounting for the composition of recipients is material to the evaluation of UI's insurance-incentive trade-off.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.012 | 0.016 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it