You Can Take it with You! The Returns to Foreign Human Capital of Male Temporary Foreign Workers*
Bibliographic record
Abstract
The research on immigration has found falling labor market outcomes of immigrants in many Western countries. In Canada, one of the major causes has been the decline in the returns to foreign work experience. Using the 1991, 1996 and 2001 Canadian Census Master Datafiles and applying both parametric and semiparametric techniques, it is found that unlike recently landed male immigrants, temporary foreign workers have no difficulty transferring their human capital to the Canadian labor market and in particular, they obtain very high returns to their foreign work experience. This is even true for temporary foreign workers from non-traditional backgrounds, a group that has had particular difficulty receiving returns to their foreign work experience for recent immigrant cohorts and now composes the majority of Canada’s immigration. It is likely that this premium can be partially attributed to the different selection process that temporary foreign workers and immigrants enter Canada under. While immigrants for the most part are selected by the government, the selection process for temporary foreign workers is driven by employers and employers may be better able to assess the
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".