Bibliographic record
Abstract
Nous cherchons à savoir si la finance influence l’impact des institutions du marché du travail sur le chômage. A partir d’un échantillon de 18 pays de l’OCDE entre 1980 et 2004, nous estimons un modèle VAR en panel. Nous vérifions si les causalités des variables du marché du travail (niveau de réglementation, densité syndicale, degré de coordination des négociations salariales) vers le chômage sont affectées par l’introduction dans l’estimation de variables financières (capitalisation du marché des actions, crédit intermédié, concentration bancaire). En Australie, en Belgique, en Italie, au Japon et en Espagne, la prise en compte de facteurs financiers diminue le bénéfice de la flexibilisation du marché du travail ou la rend néfaste à l’emploi. En Autriche, au Canada, en Finlande et au Portugal, les variables financières diminuent ses effects négatifs ou la rendent bénéfique pour l’emploi. En Irlande et aux Pays-Bas, les deux effets prévalent, selon l’institution du marché du travail étudiée. Codes JEL: E24, J23, P17. Mots clés: Chômage, marché du travail, variables financières, interactions institutionnelles, VAR en panel. We explore whether finance influences the impact of labour market institutions on unemployment. Using a data set of 18 OECD countries over 1980-2004, we estimate a panel Vector AutoRegressive model. We check whether causalities from labour market variables (labour market regulation, union density, coordination in wage bargaining) to unemployment are affected by the introduction of financial factors (stock market capitalisation, intermediated
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.003 |
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".