Heterogeneity in the Gender Wage Gap in Canada
Bibliographic record
Abstract
There is significant heterogeneity in the male-female wage gap depending on individuals’ education, income, and labour supply choices. Using data from the Canadian Census and from the Labour Force Survey, we document to what extent the gap in hourly wages gets compounded by a gender gap in hours worked, making the annual gender pay gap much larger. Within fulltime full-year, full-time part year, and part-time jobs, we find much smaller gaps than the overall one, even conditional on detailed occupations. This suggests a different selection by gender into full-time and part-time jobs, with women of higher earnings potential selecting into part-time work. We document that men are more likely to be promoted than women, regardless of marital status, while women are more likely to select into part-time jobs or be absent from work if they have children in their care. Furthermore, the wage gap is very small for younger people and it increases with age, even for single individuals, providing suggestive evidence for statistical discrimination. The male-female wage gap decreases with education, at all quantiles of the income distribution, except for a glass ceiling effect observable for the top 10% of the university wage distribution. We look more deeply at this glass ceiling effect by assigning gender to the individuals on Ontario’s Sunshine list of public salary disclosure for top earners. We document a gender imbalance on the list, with twice more men than women making the list, but no substantive gender wage gap. Given all these findings, we contend that wage equality in the labour market can only be achieved in conjunction with gender equality in the household, and that effective policies to target the remaining wage gap should address labour supply and child rearing channels.
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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 | 0.005 | 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".