Leaving and Coming Back to Canada : Evidence from Longitudinal Data (Working Paper 32)
Bibliographic record
Abstract
The paper exploits the unique strengths of Statistics Canada's Longitudinal Administrative Database ("LAD"), constructed from individuals' tax records, to shed new light on the extent and nature of the emigration of Canadians to other countries and their patterns of return over the period 1982-1999. The empirical evidence begins with some simple graphs of the overall rates of leaving over time, and follows with the presentation of the estimation results of a model that essentially addresses the question: "who moves?" The paper then analyses the rates of return for those observed to leave the country - something for which there is virtually no existing evidence. Simple return rates are reported first, followed by the results of a hazard model of the probability of returning which takes into account individuals' characteristics and the number of years they have already been out of the country. Taken together, these results provide a new empirical basis for discussions of emigration in general, and the brain drain in particular. Of particular interest are the ebb and flow of emigration rates observed over the last two decades, including a perhaps surprising turndown in the most recent years after climbing through the earlier part of the 1990s; the data on the number who return after leaving, the associated patterns by income level, and the increases observed over the last decade.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".