Relative Price Movements and Labour Productivity in Canada: A VAR Analysis
Bibliographic record
Abstract
In recent years, the Canadian economy has been affected by strong movements in relative prices brought about by the surging costs of energy and non-energy commodities, with significant implications for the terms of trade, the exchange rate, and the allocation of resources across Canadian sectors and regions. While the energy and mining industries have benefited from these movements, the pressure on the manufacturing sector has intensified, since many firms in this sector were already dealing with growing competition from low-cost economies such as China. The adjustments undertaken within the Canadian economy are readily noticeable through investment decisions, as well as through production and employment reallocation. Using vector autoregressive techniques, the authors examine how an appreciation in commodity prices and the subsequent reallocation of resources across sectors will affect hours worked and output growth and, ultimately, aggregate and sectoral labour productivity growth in Canada. Results suggest that the impact of a positive relative price shock will – in the adjustment process – lower productivity growth in the primary and the non-tradable sectors, and increase it somewhat in the manufacturing sector. The overall impact appears to be slightly negative on aggregate labour productivity growth, but this effect is only temporary.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".