Language diversity, gender inequality, and aggregate productivity in Canada
Bibliographic record
Abstract
Abstract Identifying sources of barriers to occupational mobility is central to evaluating the efficiency of the labor market. In this paper, we present an augmented Roy model in which workers self‐select into occupations, subject to labor market barriers that are specific to their socio‐linguistic group. Our findings provide evidence of differences in labor market outcomes across gender and linguistic groups in Canada. We argue that these differences could be attributed to barriers to labor mobility and that the reduction of these disparities would result in higher aggregate productivity in the country. In the augmented Roy model, the frictions result in inefficient allocation of labor across occupations. The study quantifies the change in aggregate productivity that would result from reducing the friction. The elimination of the frictions increased aggregate output on average by 6.2% and 4.4% in 1991 and 2011, respectively. This finding highlights the importance of reducing language barriers and gender inequality in the labor market. We provide policy recommendations for mitigating the disparities in socioeconomic outcomes between gender and linguistic groups.
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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 | 0.000 | 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".