Bibliographic record
Abstract
Concerns about Canada’s lackluster productivity growth have made it a priority on the public policy agenda. In this paper we argue that we should not automatically interpret declines, or slower growth rates, in aggregate labour productivity as deterioration in living standards because changes in an economy’s terms of trade are also important. Low rates of productivity growth may be the result of welfare improving changes in a country’s terms of trade that shift labour to sectors with declining labour productivity. We use the Generalized Exactly Additive Decomposition (GEAD) procedure to show how 15 sectors have contributed to aggregate business sector productivity growth in Canada from 1997 to 2019. This procedure separates a sectors’ contributions to aggregate productivity growth through relative output price changes, as well as within-sector labour productivity effects and labour reallocation effects. This analysis shows that labour productivity in Canada’s business sector increased by 30.7 percent between 1997 and 2019, an average annual growth rate of 1.2 percent. Finance, Insurance, and Real Estate (FIRE) made the largest contribution to aggregate labour productivity growth and followed by the Mining, Oil and Gas Extraction sector. The only sector that made a negative contribution to aggregate productivity growth was Manufacturing because of declines in the relative price of manufactured goods and the sector’s share of total labour input. Panel regression models indicate that a change in relative prices can indirectly influence a sector’s output per hour through changes in labour inputs. In three resource-based sectors and five service sectors, labour productivity declines when its share of labour input increases in a given year.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".