The Devaluation of Feminized Occupations in Canada, 1991-2021
Bibliographic record
Abstract
Gendered occupational segregation remains one of the most persistent drivers of the gender wage gap. Devaluation theory holds that when occupations feminize, they become devalued and face wage penalties. While evidence from the United States supports this theory, Canadian research is scarce, and existing studies suggest that stronger institutional protections and higher levels of unionization protect against devaluation. This study provides the first longterm analysis of occupational devaluation in Canada, using a 30-year intersectional lens to examine whether feminization lowers wages over time and how visible minority and immigrant representation shape this relationship. Using Canadian Census and National Household Survey data from 1991 to 2021, I construct a novel panel of occupation–industry groups and estimate a series of panel regression models, including fixed effects with temporal ordering, interaction terms, and threshold models of feminization. Across all models, feminization is associated with significant wage penalties; moving from 0% to 100% women in an occupation–industry group predicts 17.9% lower wages. These penalties intensify at higher levels of feminization, becoming most pronounced when women constitute more than 75% of an occupation. When feminization coincides with high visible minority concentration, the predicted penalty rises to 41%, indicating compounded devaluation in racialized, feminized occupations. The study’s combination of longterm national analysis, an intersectional approach, and threshold identification provides clear evidence that gendered and racialized devaluation jointly depress wages, embedding substantial and persistent inequalities within Canada’s wage-setting structures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".