Internal migration, asymmetric shocks, and interprovincial economic adjustments
Bibliographic record
Abstract
This article provides an empirical analysis of the role of labor mobility in the intranational (interprovincial) macroeconomic adjustment process in Canada. This analysis is based on a pooled time-series cross-section econometric setup of net migration flows across age groups between the ten Canadian provinces since 1977. The results indicate that interprovincial migra-tion is driven by structural factors such as the long-run regional differential in unemployment rates, labor productivity, and the rural/urban differential structure of the provinces. Further-more, it appears that interprovincial migration is not that sensitive to regional asymmetric shocks at the business cycle horizon. Finally, using a conditional convergence model of human capital, the author estimates that migration has a powerful effect on the redistribution of human capital across Canadian provinces. With the interprovincial migration process, human capital is redistributed from the more rural to the predominantly urban provinces and from the poor to the rich provinces.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".