When Pay Equity Policy Is not Enough: Persistence of the Gender Wage Gap Among Health, Education, and STEM Professionals in Canada, 2006‒2016
Bibliographic record
Abstract
Abstract This study examines gender, geographic, and earnings inequalities within and across 13 health, education, and STEM (science, technology, engineering, and mathematics and computer science) professions in Canada. Data from the 2006 and 2016 population censuses were pooled and linked to a continuous geospatial remoteness index for assessing trends in occupational feminization and associated employment earnings among degree-holding professionals aged 25–54. Linear regression and Oaxaca-Blinder decomposition methods were used to analyze how personal, professional, and socioenvironmental factors may attenuate or magnify wage differentials by sex. Results show the STEM professions tended to remain male-dominated, heavily urbanized, and subject to significantly lower earnings for women compared to men. Other historically female-dominated professions, notably nursing professionals and secondary school teachers, were characterized with geographic distributions most closely approaching the general population, relatively narrower gender wage gaps, but also lower average annual earnings. A significant gender wage differential was found in each profession, with women earning 4.6‒12.5% less than men, after adjusting for traditional human capital measures, social characteristics intersecting with gender, and community remoteness and accessibility. Residential remoteness and census period generally explained little of the gender wage gap. Despite decades of pay equity policies in Canada, women’s earnings averaged 2.3‒7.9% less than men’s due to unexplained factors, a finding which may be attributed, at least in part, to persistent (unmeasured) gender discrimination even in highly educated professions.
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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 | 0.001 | 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 | 0.001 | 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".