Cities and Growth: Moving to Toronto - Income Gains Associated with Large Metropolitan Labour Markets
Bibliographic record
Abstract
This paper examines the process by which migrants experience gains in earnings subsequent to migration and, in particular, the advantage that migrants obtain from moving to large, dynamic metropolitan labour markets, using Toronto as a benchmark. There are two potentially distinct patterns to gains in earnings associated with migration. The first is a step upwards in which workers realize immediate gains in earnings subsequent to migration. The second is accelerated gains in earnings subsequent to migration. Immediate gains are associated with obtaining a position in a more productive firm and/or a better match between worker skills and abilities and job tasks. Accelerated gains in earnings are associated processes that take time, such as learning or job switching as workers and firms seek out better matches. Evaluated here is the expectation that the economies of large metropolitan areas provide workers with an initial productive advantage stemming from a one-time improvement in worker productivity and/or a dynamic that accelerates gains in earnings over time through the potentially entwined processes of learning and matching. A variety of datasets and methodologies, including propensity score matching, are used to evaluate patterns of income gains associated with migration to Toronto.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".