The Effects of a Training Levy on Training Characteristics and Outcomes: The Case of Quebec
Bibliographic record
Abstract
In this article, we compare the characteristics of workplace-provided training and its effects on wages in Quebec with other Canadian provinces. It is widely argued that training tends to be under-provided by employers. The institutions of training provision vary across Canada. Quebec is most distinct. With Law 90, it attempted to address what was seen as a distinctly severe problem of under-supply; it required that firms invest a specified proportion of their sales in training or turn over the difference between what they spent and the specified proportion to the government. We use data from the Workplace and Employee Survey to explore the possible effects of this measure. There are differences between Quebec and the other provinces in the incidence of on-the-job and formal training, and in the relations between training and the wage rate. In Quebec, the incidence of on-the-job training is distinctly low and the use of external training providers distinctly high. We suggest that these outcomes are encouraged by Law 90, which encourages employers to use readily documentable forms of training. We also find that the association between on-the-job training and the wage rate is much stronger in Quebec than in the comparator provinces. We argue that this is probably because, being less abundantly provided, on-the-job training is likely to be provided to employees who, on average, are more talented than their counterparts in the rest of Canada. We set our discussion of the possible effects of Law 90 in the context of a broader consideration of the relation between institutions and training choices and outcomes, including international comparison.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".