Evolution of the gender earnings gap among Canadian university graduates
Bibliographic record
Abstract
This paper reports the results of an empirical analysis of the gender earnings gap among recent Canadian Bachelor's level university graduates. The overall gap as of two years leaving university narrowed significantly across successive cohorts of graduates, but widened significantly from two to five years out for all groups. Differences in the explanatory variables ‘explain’ account for between 40% and essentially the entire gap across the different periods, this portion rising from two to five years out and across cohorts. By the final group, all of the gap is thus ‘explained’ at the two-year point in time, and most of it is explained at the five-year mark, meaning that labour market returns (measured in this manner) are largely gender-neutral for the last group of graduates. Hours of work is the single most important influence, while past work experience, job characteristics, family status, and province of residence and language have smaller and more mixed effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".