Foreign Scientists and Engineers and Economic Growth in Canadian Labor Markets
Bibliographic record
Abstract
In this paper we analyze the impact of foreign-born workers in the fields of Science, Technology, Engineering and Math (STEM) on employment and wages in Canadian geographical areas during the period 1991-2006. Canadian policies select immigrants with a strong emphasis on high educational attainment. Moreover the foreign-born constitute a third of the Canadian population making Canada a very good case to analyze the effect of foreign-STEM workers on the local economy. We use the dispersion of immigrants by nationality across 17 geographical areas in 1981 to predict the supply-driven increase in foreign Scientists and Engineers during the period 1991-2006. Then we analyze their impact on the employment and wages of college and non-college educated Canadian-born (native) workers. We find significant positive effects on the wages and (to a lesser extent) employment of college educated natives. We also find a smaller positive effect on the wages and employment of native workers with very low levels of education (i.e. those with no high school degree). This implies a positive productivity effect of foreign-STEM workers in Canada, and also a college bias in their contribution to productivity growth. Compared to the effect of foreign Scientists and Engineers in US cities, the Canadian results show similar effects on wages of college educated and at least partial evidence of a positive diffusion of the effect to non-college educated, which was not present in the US.
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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 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.006 | 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".