Immigrant Success in the Knowledge Economy: Institutional Change and the Immigrant Experience in Canada, 1970–1995
Bibliographic record
Abstract
This research examines how institutional changes associated with the emergence of a “knowledge economy”—specifically the expansion of education and the changing labor market structure —shaped employment experiences of newly arriving immigrants to Canada over the period 1970–1995. Census data on successive cohorts of immigrant men and women (from microdata files for 1981, 1986, 1991, and 1996) show a progressive trend toward lower rates of labor force participation and lower levels of earnings relative to the native‐born population, both overall and for most specific origins groups. These trends are only partly attributable to business cycle fluctuations in labor demand. The present article examines the impact of selected educational and labor market changes on successive cohorts of immigrants, using intertemporal substitution methodology. The analysis finds that (1) increased native‐born education levels infringe upon the traditional immigrant education advantage, outpacing effects of increased immigrant skill selectivity; (2) increased returns to education among native‐born workers do not apply to immigrants; and (3) other institutional obstacles to immigrant success also exist. The declining relative value of immigrant education may be due to the location‐specific nature of credential validation processes. Directions for further research and policy analysis are suggested.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".