Unemployment Benefits vs. Unemployment Accounts: A Quantitative Exploration
Bibliographic record
Abstract
We use a dynamic equilibrium model with heterogeneous agents to assess the quantitative effects of switching from the typical unemployment insurance programs to a system of mandatory unemployment accounts. In such a system, workers contribute to a personal account from which they can withdraw, in a controlled fashion, once unemployed or retired. One of the promises of this alternative approach to unemployment benefits is its effect on moral hazard and shirking as workers are personally affected when they turn down offers. However, the account system may lead to new distortions. Calibrating the model to Oregon, we answer the following questions: 1) What scheme specification would be optimal from a social planner’s perspective? 2) How does such an optimal scheme compare to current unemployment insurance programs? 3) How can things go wrong in a poorly tuned system? Previous versions of this paper benefited from comments from seminar participants at the 2007 SED meeting in Prague, 2007 SCE meeting in Montreal, 2008 Midwest Macro Meetings at UPenn, Universite du Maine, Universite d’Aix-Marseille, OECD, IZA, Humboldt Universitat Berlin, University of Connecticut, Rimini Center, University of Saskatchewan, University of Waterloo and Federal Reserve Bank of Atlanta. This work was partially sponsored by the Cascade Policy Institute. We thank the Institute for giving us complete liberty in the conduct of this research. The views expressed are those of the individual authors and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".