Recent Immigration to Canada and the United States: A Mixed Tale of Relative Selection
Bibliographic record
Abstract
Using large-scale census data and adjusting for sending-country fixed effect to account for changing composition of immigrants, we study relative immigrant selection to Canada and the U.S. during 1990-2006, a period characterized by diverging immigration policies in the two countries. Results show a gradual change in selection patterns in educational attainment and host country language proficiency in favor of Canada as its post-1990 immigration policy allocated more points to the human capital of new entrants. Specifically, in 1990, new immigrants in Canada were less likely to have a B.A. degree than those in the U.S.; they were also less likely to have a high-school or lower education. By 2006, Canada surpassed the U.S. in drawing highly-educated immigrants, while continuing to attract fewer low-educated immigrants. Canada also improved its edge over the U.S. in terms of host-country language proficiency of new immigrants. Entry-level earnings, however, do not reflect the same trend: recent immigrants to Canada have experienced a wage disadvantage compared to recent immigrants to the U.S., as well as Canadian natives. One plausible explanation is that, while the Canadian points system has successfully attracted more educated immigrants, it may not be effective in capturing productivity-related traits that are not easily measurable.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".