Temporary foreign workers and firms: Theory and Canadian evidence
Bibliographic record
Abstract
The goal of our paper is to better understand the economic implications of Temporary Foreign Worker (TFW) programs as well as comprehend the underlying reasons for the rapid expansion of the number of TFWs hired by employers under the Canadian program brought to light in 2014. We present an efficiency wage model that allows for the possibility that a firm, unable to find a worker after advertising for a period of time, may hire a TFW at the advertised wage. Due to the assumed lower outside option for the TFW than the domestic worker, the TFW will exert higher effort than a domestic worker even if the TFW is paid the same wage as would have been paid to a domestic worker. In equilibrium, lower wage offers are made to less-skilled domestic workers when a TFW program of this kind is in place. The model also implies higher unemployment rates for domestic workers after the introduction of a TFW program. Our empirical analysis is based on the confidential master files of the Canadian Census (1991-2006) and the Labour Force Survey (2006-2013). We find that TFWs in Canada work longer hours, have lower rates of absenteeism, and are less likely to be laid off, consistent with higher effort in our model. Moreover, TFWs work at lower wage rates than domestic workers even for similar job characteristics, which is also a prediction of our model.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".