Decomposing Multifactor Productivity Growth in Canada by Industry and Province, 1997- 2014
Bibliographic record
Abstract
Between 1997 and 2014, multifactor productivity (MFP) in Canada's business sector industries grew at an annual rate of 0.02 per cent per year - essentially zero. In this article, we decompose aggregate MFP growth into contributions by industry and province. Two sets of results are presented: one based on the generalized exactly additive decomposition (GEAD) and one based on the CSLS decomposition. The two decomposition methods lead to very different conclusions. The GEAD suggests that the reallocation of inputs to the mining and oil and gas extraction industry in the oil-rich provinces were the primary drivers of MFP growth in Canada while the manufacturing sector, concentrated in Ontario and Quebec, dragged MFP growth down. The CSLS decomposition suggests precisely the opposite: mining and oil and gas was the main hindrance to Canada's MFP performance while manufacturing was the major driver of MFP growth. The disagreement between the two methods is primarily attributable to the fact that the large increase in commodity prices (especially oil prices) over the 1997-2014 period increases the mining and oil and gas industry's contribution to MFP growth according to the GEAD while the CSLS decomposition does not treat such relative price effects as contributors to productivity growth.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".