The Impact of Employment Protection Mandates on Demographic Temporary Employment Patterns: International Microeconomic Evidence
Bibliographic record
Abstract
Using 1994-98 International Adult Literacy Survey (IALS) microdata, this paper investigates\nthe impact of employment protection laws on the incidence of temporary employment by\ndemographic group. More stringent employment protection for regular jobs is predicted to\nincrease the relative incidence of temporary employment for less experienced and less\nskilled workers. I test this reasoning using IALS data for Canada, Finland, Italy, the\nNetherlands, Switzerland, the United Kingdom and the United States, countries with widely\ndiffering levels of mandated employment protection. Across these countries, the strength of\nsuch mandates (as measured by the OECD) is positively associated with the relative\nincidence of temporary employment for young workers, native women, immigrant women and\nthose with low cognitive ability. These effects largely hold up when I adjust for the possible\nsample selection due to the fact that employment to population ratios differ across countries.\nMoreover, the effects of protection on the young, women, and immigrants are stronger in\ncountries with higher levels of collective bargaining coverage, suggesting a connection\nbetween binding wage floors and the allocative effects of employment protection mandates.
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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 | 0.013 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".