Temporary Foreign Workers in Canada: Are They Really Filling Labour Shortages?
Bibliographic record
Abstract
Since easier access to a large supply of foreign labour might generate undesirable incentives on the part of both employers and prospective workers, a Temporary Foreign Worker (TFW ) program requires careful design. Failure at any stage of the process – at time of hiring, during employment, or at the end of the contract – is likely to create significant negative effects on domestic workers and, in the medium term, on the temporary foreign workers themselves. When choosing between domestic and foreign workers, employers are naturally concerned about labour costs and labour productivity. Therefore, a key design feature of any TFW program is the hiring conditions it imposes on employers – conditions that must deal with regional or occupational labour market shortages. Between 2002 and 2013, Canada eased the hiring conditions of TFWs several times, supposedly because of a reported labour shortage in some occupations, especially in western Canada. By 2012, the number of employed TFWs was 338, 000, up from 101, 000 in 2002, yet the unemployment rate remained the same at 7.2 percent. Furthermore, these policy changes occurred even though there was little empirical evidence of shortages in many occupations. When controlling for differences across provinces, I find that changes to the TFWP that eased hiring conditions accelerated the rise in unemployment rates in Alberta and British Columbia. The reversal of some of these changes in 2013 is welcome but probably not sufficient, largely because adequate information is still lacking about the state of the labour market, and because the uniform application fee employers pay to hire TFWs does not adequately increase their incentive to search for domestic workers to fill job vacancies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".