Explaining the earnings disadvantage of visible minority immigrants in Canada
Bibliographic record
Abstract
This dissertation is manuscript-based. It contains an introduction, a literature review, a conclusion along with the four research papers that constitute its core. The four substantive papers reexamine the sources of earnings differences by race and immigration status in Canada. I address two major methodological issues in the relevant literature: the measurement of experience and the modeling of the relations between the factors known to influence earnings. Data from Statistics Canada's Workplace and Employee Survey (WES) was analyzed. The first two papers examine biases in the estimates of wage disparities due to error in the measurement of experience. They do so using two conventional estimation techniques: ordinary least squares (OLS) with dummy variables, and the Blinder-Oaxaca decomposition. The third and fourth papers explore deficiencies in OLS-based modeling techniques. The third paper does so by separately analyzing the relationships between racial and immigrant group statuses and access to job-related training, and then the relationship between statuses and training, on one hand, and earnings on the other. The final paper uses structural equation modeling to further examine the relationship between group status and earnings, this time explicitly incorporating the mediating effects of job types, and job-related training. The papers reveal that inadequate measurement of work experience results in overestimates of the wage disadvantage of visible minority immigrants. Furthermore, some of the wage disadvantage of this group stems from limited access to job-related training.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".