Occupational Attainment And The Earnings Of Native-Born And Foreign-Born Canadians
Bibliographic record
Abstract
The economic performance of immigrants has been studied primarily in terms of entry earnings and how these earnings evolve over time in the host country. The empirical analysis typically revolves around variants of an earnings function, which relates worker earnings to human capital and other individual characteristics. In this literature, the effects of occupational attainment on earnings are typically not modelled mainly because occupation is perceived as just another way of looking at earnings. However, as noted by Chiswick and Miller (2008), amongst others, occupation is the channel through which an individuals human capital translates into earnings. That is, education has both a direct impact on earnings and an indirect one operating through occupational status. Empirical findings for the US and Australia provide support for this view. Our objective in this paper is to extend this analysis to Canada, to assess how the earnings gains from human capital depend upon occupational status for both native-born and immigrant workers, and upon the length of residence of the latter in Canada. This will also shed light on the relative importance of the intra-occupational vis-a-vis inter-occupational mobility of immigrants in realizing earnings gains from education, in the short and longer term. The paper assesses these issues by looking at data drawn from the 2001 Canadian census.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".