The Geography of Employment Growth in Western Canada: A Regional Typology based on Multifactor Partitioning
Bibliographic record
Abstract
Canada's employment growth, 2001-2006, masked large regional variations.Such disparities have been a major policy concern since the (British) Royal Commission on the Distribution of the Industrial Population (Barlow Report 1940) which introduced shift-share analysis and identified industry-mix as the principle determinant of regional disparities (Jones 1940).This paper uses the Ray-Srinath multifactor partitioning (MFP) model, an advanced shift-share methodology, to extract the region, industry-mix and net interaction effects on regional employment growth in Canada 2001-2006 and presents the results for the thirty economic regions of Western Canada.All three effects are important.However, it is the region effect, not industry-mix, which has most affected employment growth in Canada 2001-2006.Indeed, no region with a low region effect exceeded the national employment growth rate.But some regions with a very good industry-mix failed to reach the national growth rate because of their poor region effect.The MFP results are mapped and used to allocate the economic regions of Western Canada to the Biffignandi regional typology.Seven main regional development types are identified.The top class is the "regions of general employment expansion".The Calgary-Edmonton corridor is in this class: Calgary scored a triple "A" rating, excelling on all three growth effects.Its employment growth rates were among the very highest in the country.At the other extreme, some peripheral areas lagged on all three components and experienced employment decline.The paper concludes with some policy implications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".