Convex Earnings, Childcare, and the Gender Pay Gap: An Exploratory Study of Canadian College Graduates
Bibliographic record
Abstract
In the last ten years labor economists have become increasingly interested in convex earnings structures in the labor market and their impact on the gender pay gap, an approach spearheaded by Claudia Goldin in her 2014 Presidential Address to the American Economic Association. The study has seen widespread acclaim, but very little replication. This study replicates parts of Claudia Goldin’s work, applying it to college graduates in Canada and in five subnational provinces and regions. First, using 2015-2017 releases of the Canadian Labour Force Survey, it plots the gender pay gap residual across different age categories. Second, it investigates the correlation between the pay gap residual and convex earnings structures as proxied by the elasticity of income with respect to work hours. Third, it considers this correlation separately for individuals with and without children, and in light of provincial differences in childcare patterns, as recorded by Statistics Canada’s General Social Survey on Time Use. The study does not attempt to establish statistical significance, but instead uses descriptive statistics to sketch an outline of patterns that would need further analysis to confirm. The results suggest that there is a gender pay gap present among college graduates in Canada that cannot be explained by age, profession, tenure or the raw number of work hours. There is mixed evidence that this can be explained by convex earnings structures for the population at large, though the evidence is stronger when only individuals with children are considered.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".