Bibliographic record
Abstract
We characterize optimal income taxation and unemployment insurance in a search-matching framework where both voluntary and involuntary unemployment are endogenous and Nash bargaining determines wages. Individuals differ in utility when voluntarily unemployed (non-participants in the labour market) and decide whether to participate as a job seeker and if so, how much search effort to exert. Unemployment insurance trades of insurance versus moral hazard due to search. We show that it is optimal to have a positive linear wage tax without any redistributive concerns even if search is effcient so the Hosios condition is satisfied. We also allow for different productivity types so there is a redistributive role for the income tax and show that a proportional wage tax internalizes the macro effects arising from endogenous wages. Lump-sum income taxes and transfers can then redistribute between individuals of differing skills and employment states. Our analysis embeds optimal unemployment insurance into an extensive-margin optimal redistribution framework where transfers to the involuntary and voluntary unemployed can differ, and nests several standard models in the literature.
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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 | 0.001 | 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 | 0.001 |
| 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".