Does Migration Reduce the Wage Penalty from Graduating College in a Recession? Evidence from Atlantic Canada
Bibliographic record
Abstract
Atlantic provinces have long suffered from brain drain: young people leaving the area for more and better-paying work elsewhere in Canada. The pressure to leave is especially strong during economic downturns, such as the Great Recession of 2008. The economic costs of this process are considerable: the break-up of families, the loss of skills, ideas and innovation, and the decline of critical services like health and education. BUT IS IT WORTH IT TO LEAVE? Our report, which studies the employment patterns of “Great Recession” graduates for five years, shows that though graduates that stay home initially have lower salaries than those that emigrate, over time their salaries increase faster than the “leavers.” In other words, they eventually catch up to the higher salaries offered in other provinces. How can these provinces encourage recent graduates to stay? Our report discusses policy solutions, including adopting higher tuition rates for non-Maritime students, and providing wage subsidies to companies to increase their staff—particularly staff with STEM backgrounds. This report, Does migration reduce the wage penalty from graduate college in a recession? provides an extensive technical analysis of data related to patterns of employment and migration for recent graduates in Atlantic provinces after The Great Recession. For a summary of the findings, consult our companion policy report.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.008 | 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".