Interprovincial Migration in Canada: Implications for Output and Productivity Growth, 1987-2014
Bibliographic record
Abstract
There were slightly more than 300, 000 interprovincial migrants in Canadain 2014, representing 0.85 per cent of the population. Interprovincial migrationprovidessignificant economic benefits by reallocating labour from low-productivity regions with high unemployment to high productivity regions with low unemployment.A previous report released by the Centre for the Study of Living Standards estimated the impact of net interprovincial migration on aggregate output and productivity between 1987 and 2006.This study uses the same basicmethodology to provide updated estimates, which is extendedto estimatethe long-term effects.We estimatethat interprovincial migration raised GDP by $1.23 billion (chained 2007 dollars) in 2014, or 0.071 per cent of GDP. This may seem like a small amount, but migration flows are often persistent. We estimate that cumulative net migration flows over the 1987-2014 period increased GDP by $15.8 billion dollars(0.9 per cent of GDP) in 2014and generatedcumulative benefits of $146 billionover the 1987-2014 period.Mostof these gains can be attributed tomigration toAlbertaand British Columbia, which areby far the largest destinationsof net interprovincial migration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".