Mandatory Severance Pay: An Assessment of its Coverage and Effects in Peru *
Bibliographic record
Abstract
In Peru, like in many other developing countries, employers have the legal obligation to compensate workers who are dismissed due to no fault of their own. Is this an efficient mechanism to provide income support to the unemployed? This paper seeks an answer to this question using individual records from a household survey with a panel structure. Relying on five coverage indicators, the paper shows that roughly one in five private sector workers, and one in three private sector wage earners, is legally entitled to severance pay. Coverage is more prevalent among wealthier workers. Results based on several empirical strategies suggest that workers “pay ” for their entitlement to severance pay through lower wages. Finally, consumption among unemployed workers who receive severance pay is 20 to 30 percent higher than among those who do not. Consumption among these workers is actually higher than among those who are employed, implying that mandatory severance pay is excessively generous in Peru. ________________ * This paper was written for a broader World Bank study on the management of economic insecurity in Latin America and the Caribbean, with support from a Canadian Trust Fund
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".