Unemployment Insurance Eligibility, Moral Hazard and Equilibrium Unemployment
Bibliographic record
Abstract
This paper shows that the Mortensen-Pissarides search and matching model can be successfully parameterized to generate observed large cyclical fluctuations in unemployment and modest responses of unemployment to changes in unemployment insurance (UI) benefits. The key features behind this success are the consideration of the eligibility for UI benefits and the heterogeneity of workers. With the linear utilities commonly assumed in the Mortensen-Pissarides model, a fully rated UI system designed to prevent moral hazard has no effect on unemployment. However, the UI system in the United States is neither fully rated nor able to prevent workers with low productivity from quitting their jobs or rejecting employment offers to collect benefits. As a result, an increase in UI generosity has a positive, but realistically small, effect on unemployment. This paper answers the Costain and Reiter (2008) criticism to the Hagedorn and Manovskii (2008) strategy of adopting a high value of non-market activities to generate realistic business cycles with the Mortensen-Pissarides model.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".