QED Queen’s Economics Department Working Paper No. 1285 Immigrant Selection Systems and Occupational Outcomes of International Medical Graduates in Canada and the United
Bibliographic record
Abstract
We analyze the process of immigrant selection and occupational outcomes of Inter-national Medical Graduates (IMGs) in the US and Canada. We extend the IMG relicensing model of Kugler and Sauer (2005) to incorporate two different approaches to immigrant selection: employer nomination systems and point systems. Analysis of the model indicates that point systems can allow IMGs to immigrate who would be unable to gain entry to the receiving country under an employer nomination system and who are subsequently unable to relicense and work as physicians in the receiving country. We apply the model to the case of IMGs migrating to the US and Canada since the 1960s and evaluate the empirical predictions from the model based on an analysis of the occupational outcomes of IMGs in Canada (where a point system has been in place) and in the US (where IMGs enter through employer nomination). In Canada, IMGs are less likely to be employed as a physician than are IMGs in the US and a large percentage of the IMGs in Canada either find work in lower skill occupa-tions or are not employed. The empirical findings are consistent with our hypotheses
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.091 | 0.006 |
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".