Sickness and injury leave in France: moral hazard or strain?
Bibliographic record
Abstract
From 1997 to 2001, the total payment to compensate for sickness and injury leaves increased dramatically in France. Since this change coincided with a decrease in unemployment rate, three hypothesizes should be proposed as possible explanations consistently with the literature: moral hazard (workers fear less to loose their job, therefore use sickness leave more confidently); strain (workers work longer hours or under more stringent rules); labor-force composition effect (less healthy individuals are incorporated into the labor force). We investigate the first two strands of explanation using a household survey (ESPS) enriched with claims data from compulsory health insurance funds on sickness leaves (EPAS). We model separately number of leaves per individual (cumulative logit) and duration of leaves (random-effect model). According to our findings, in France, the individual propensity to take sickness leave is mainly influenced by strain in the workplace and by a labor-force composition effect. Conditional duration of spells is not well explained at the individual level: the only significant factor is usual weekly work duration. Influence of moral hazard is not clearly ascertained: it has few impact on occurrences of leave and no impact on duration.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".