Les institutions du marché du travail et les inégalités inter-catégorielles: une comparaison France-Canada
Bibliographic record
Abstract
The rising disparities, to the detriment of less qualified workers, is certainly one of the most worrisome trends to which the developed countries have to face. Nevertheless, this deteriorating didn't have the same effects in all the countries. Indeed, in the Anglo-Saxon countries, the incomes disparities became more significant whereas in the countries of beveridge tradition, we talk about unemployment rate inequalities. The object of this article is, on the one hand, to propose a reflection on the causes and the explanations of this evolution unfavourable to the less qualified categories. On the other hand, using a comparison between France and Canada, we study the interaction between the labour market public policies and the disparities nature in each of both countries. In this perspective, we compare the effects of three public regulations, namely unemployment benefits, minimum wage and negative income tax, on the labour market performances and more particularly on employment and earnings. The entire document will be available soon. L'evolution des inegalites, au detriment de la main d'uvre non qualifiee, est certainement l'une des tendances les plus preoccupantes parmi celles auxquelles ont a faire face les grandes economies occidentales. Neanmoins, cette degradation ne s'est pas traduite de la meme facon dans tous les pays. En effet, dans les pays anglo-saxons, les ecarts de revenus se sont accentues tandis que dans les pays de tradition beveridgienne, on parle des inegalites en termes d'acces a l'emploi. L'objet de ce rapport est, d'une part, de proposer une reflexion sur les causes et les explications de cette evolution defavorable aux categories les moins formees. D'autre part, dans le cadre d'une comparaison France-Canada, nous allons etudier l'interaction entre les politiques publiques sur le marche du travail et la forme des inegalites presente dans chacun des deux pays. Dans cette perspective, nous comparons les effets de trois regulations publiques, a savoir les allocations chomage, le salaire minimum et l'impot negatif, sur les performances de l'economie et plus particulierement sur l'emploi et la formation des salaires. Le document complet sera disponible ulterieument.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".