Licensing Requirements and Occupational Mobility Among Highly Skilled New Immigrants in Canada
Bibliographic record
Abstract
The present study compares the occupational trajectories of highly skilled immigrants in regulated occupations to those outside of the regulated occupations, from their pre-migration occupation, to their first job in Canada, and to subsequent jobs. Licensing requirements are likely to affect new immigrants’ occupational trajectories since they have a direct effect on how employers assess qualifications. This study utilizes growth curve modeling (GCM) and a unique dataset that contains detailed information on new immigrants’ experiences in Canada: the Longitudinal Survey of Immigrants to Canada (LSIC). Our findings indicate that immigrants working in regulated occupations prior to migration who are unable to find jobs in regulated occupations in Canada face a significantly greater drop in occupational status when they first arrive than those working in unregulated professions in their home country. Furthermore, their occupational progression over time is not faster than that of their counterparts from unregulated professions. Those who worked in unregulated fields prior to migration but found jobs in regulated fields in Canada experience an improvement in their occupational status after migration. Lastly, for those who worked in regulated professions in their home country and were able to find jobs within regulated fields in Canada, initial occupational status scores are similar to their scores in their country of origin, and there is little change in occupational status with time in Canada. The results of this study highlight the importance of ensuring that the licensing process is made easier to navigate for new immigrants. Our findings clearly indicate that immigrants who are able to successfully enter a regulated profession soon after migration fare much better in terms of occupational status than those who are unable to become licensed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".