Post-Secondary Education in Canada: Can Ability Bias Explain the Earnings Gap Between College and University Graduates?
Bibliographic record
Abstract
Using the Canadian General Social Survey we compute returns to post-secondary education relative to high-school. Unlike previous research using Canadian data, our dataset allows us to control for ability selection into higher education. We find strong evidence of positive ability selection into all levels of post-secondary education for men and weaker positive selection for women. Since the ability selection is stronger for higher levels of education, particularly for university, the difference in returns between university and college or trades education decreases slightly after accounting for ability bias. However, a puzzling large gap persists, with university-educated men still earning over 20% more than men with college or trades education. Moreover, contrary to previous Canadian literature that reports higher returns for women, we document that the OLS hourly wage returns to university education are the same for men and women. OLS returns are higher for women only if weekly or yearly wages are considered instead, because university-educated women work more hours than the average. Nevertheless, once we account for ability selection into post-secondary education, we generally find higher returns for women than for men for all wage measures as a result of the stronger ability selection for men.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".