International Mobility: A Longitudinal Analysis of the Effects on Individuals Earnings
Bibliographic record
Abstract
The degree to which workers leave the country was a much-discussed issue in Canada - as elsewhere - in the latter part of the 1990s, although recent empirical evidence shows that it was not such a widespread phenomenon after all, and that rates of leaving have declined substantially in recent years. One aspect of the international mobility dynamic that has not yet been addressed, however, is the effect on individuals' earnings of leaving the country and then returning. The lack of empirical evidence on this issue stems principally from the unavailability of the kind of longitudinal data required for such an analysis. The contribution of this paper is to present evidence on how leaving and returning to Canada affects individuals' earnings based on an analysis carried out with the Longitudinal Administrative Database. The models estimated use movers' (relative) pre-departure profiles as the basis of comparison for their post-return (relative) earnings patterns in order to control for any pre-existing differences in the earnings profiles of movers and non-movers (while also controlling for other factors that affect individuals' earnings at any point in time). Overall, those who leave the country have higher earnings than non-movers upon their returns, but most of these differences were already present in the pre-departure period. In terms of net earnings growth, individuals who were away for two to five years appear to do best, and enjoy earnings that are 12% higher in the five years following their return relative to their pre-departure levels (controlling for other factors), while those who leave for just one year have smaller gains, and those who spend longer periods abroad have lower (relative) earnings upon their returns as compared to before leaving (perhaps due to other events associated with their mobility patterns). Interestingly, these gains seem to be concentrated among those who had the lowest pre-move earnings levels (less than $60, 000), while those higher up on the earnings ladder had smaller and more variable gains.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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 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".