Employment protection, temporary and permanent employment share, and procyclicality of labour productivity
Bibliographic record
Abstract
Abstract Using data from 32 Organisation for Economic Co‐operation and Development (OECD) countries, we find that the extent of the procyclicality of average labour productivity (ALP) differs significantly across countries, and is positively correlated with the strictness of employment protection. To account for this salient feature, we build up a theoretical model in which aggregate employment is divided into temporary and permanent categories; the two groups of employment are highly substitutable in production and the latter is subject to firing costs. Our numerical results suggest that in our model the intensity of labour firing costs, which characterizes the strictness of employment protection in OECD countries, has positive effects on both the procyclicality of ALP and the share of temporary employment. Moreover, considering the possibility of hiring different types of workers as short‐term substitutes contributes to a reduction in the relative volatility between output and aggregate hours worked, and hence a sharp decline in the procyclicality of ALP. The model can also explain why, with a higher intensity of labour firing costs, a firm has more incentives to hoard less productive redundant workers when the economy experiences a negative total factor productivity shock.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".