The Non-neutrality of Severance Payments with Incomplete Markets
Bibliographic record
Abstract
We study the equilibrium welfare effects of introducing mandated severance payments in a labor market with costly mobility, where self-insurance through a riskless asset is the only way to smooth fluctuations in labor income due to unemployment shocks. The framework allows for wage flexibility at the level of the individual firm-worker match. Wages vary with both tenure and productivity of the workers. When severance payments are introduced, the firm can potentially undo their effect by modifying the wage profile. Workers entry wages fall by the expected present value of the future payment. However, because of incomplete markets, workers are unlikely to be indifferent about the slope of the wage profile. Moreover, non trivial general equilibrium effects are also present, since the capital stock varies. On the one hand, the precautionary motive for savings is reduced by the introduction of severance payments, since agents are better insured. On the other hand, the change in the wage profile is likely to reduce the savings of young individuals and increase that of older ones. The model is solved numerically and calibrated to the US economy. We compare a welfare measure for the baseline economy, i.e. without severance payments, to those of a series of counterfactual economies where the severance payments are introduced at increasing levels. For reasonable values of the severance payments, Welfare gains and costs seem to be quantitatively small. JEL Classification Codes: E24, D52, D58, J65.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 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".