Do Degrees Still Pay Off? Changing Returns to Education in Canada from 2005 to 2020.
Bibliographic record
Abstract
This paper studies how returns to educational degrees has evolved in Canada from 2005 to 2020 by combining data from the 2006 and 2021 Canadian Census. The study uses a two-class finite mixture model to capture unobserved heterogeneity that may be correlated with individual characteristics, in a way that resembles the use of fixed effects in a panel data setting. The mixture model reveals two distinct groups and highlights unobserved differences in earnings that standard models could miss. The findings show that while higher education still leads to higher earnings, the return to education has empirically declined over time for individuals with graduate degrees. In contrast, returns to college and trade-level education have increased, suggesting that practical skills has become more valuable in the Canadian job market. These findings reveal the changing nature of the Canadian economy and help policymakers and educators improve education and training programs to better meet the needs of today’s job market.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".