A Work in Progress: Measuring Wage Gaps for Women and Minorities in the Canadian Labour Market
Bibliographic record
Abstract
A wide range of federal and provincial legislation is intended to ensure every Canadian has an equal opportunity to participate in and benefit from work in the Canadian labour market. Despite this, we invariably see evidence of large labour market disparities between different groups of individuals. In this Commentary, we describe the wage gaps that persist among groups working full-time in the private sector: between men and women, between individuals who are white, Indigenous, and members of visible minority groups, and between non-Indigenous individuals who were born in Canada and those who were not. We first examine the hourly wages of men and women. Women’s hourly wages in the private sector were 27 percent less than men’s in 2000. By 2019, the hourly wage gap had narrowed to 19 percent. A large part of the gap is attributed to differences in men’s and women’s job characteristics. For example, men in the private sector are more heavily represented in higher-wage industries like construction or oil and gas, while women are more heavily represented in lower-wage industries such as retail services or accommodation and food services. This segregation of men and women across industries reflects a wide array of supply- and demandside factors. We then examine the 2015 annual earnings of Canadian-born men and women who are white, Indigenous, and members of visible minority groups. After adjusting earnings gaps to account for group differences in demographic and job characteristics (including education, industry, and occupation), there remain substantial gaps between Canadian-born white men and all other groups of Canadian-born men and women. The largest gaps are observed between Canadian-born white men and Indigenous women in Canada. We further examine the gaps between Canadian-born white men and immigrants by visible minority status. Overall, the results demonstrate complex interactions between the roles played by gender, racial identity, and immigrant status in affecting labour market outcomes. For policymakers, addressing these gaps in the labour market is challenging. There are many factors underlying the earnings differences between groups, representing challenges on both the supply and demand sides of the market. Pay equity and employment equity legislation has been limited in application and effectiveness in a private sector context. Policy can be directed toward improving education and training across fields in which women, Indigenous peoples, and members of visible minority groups are underrepresented. Family-friendly policies are also important for shaping labour market opportunities.
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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 | 0.003 | 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 | 0.001 |
| 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".