Bibliographic record
Abstract
3 The level of labour productivity in Canadian manufacturing in the postwar period has been below the US level, although the extent of this gap has varied considerably over time. From 1977 to 1994 the Canada-US gap in output per hour in manufacturing averaged 14 per cent (Chart 1). Since 1994, however, Canada’s relative gap has risen 17.3 percentage points from 12.3 per cent in 1994 to 29.6 per cent in 2000 (32.3 per cent in 2001), as output per hour in Canadian manufacturing fell from 87.7 per cent of the US level in 1994 to 70.4 per cent in 2000 (and 67.7 per cent in 2001).1 This development has reflected both an acceleration of labour productivity growth in manufacturing in the United States, and a deceleration in Canada. Manufacturing accounts for about 15 per cent of total economy employment and output. The widening of the Canada-US labour productivity gap in manufacturing thus accounted for over two thirds of the widening in the aggregate Canada-US labour productivity gap in the 1990s.2 Labour productivity growth is a major long-run determinant of the growth in living standards. Had productivity growth in Canadian manufacturing matched the US rate of advance since 1994, and the productivity gap remained unchanged, growth in Canadian absolute and relative (compared to the United States) living standards would have been higher. Another reason for concern about the widening manufacturing productivity gap relates to the fact that this sector includes dynamic high-tech industries, which are important to the overall performance of the Canadian economy. In addition to the widening of the labour productivity gap, the total factor productivity (TFP) gap in manufacturing has also increased significantly.3 This gap rose 13.9 percentage points from 3.7 per cent in 1994 (96.3 per cent of the US level) to 17.6 per cent in 2000 (82.4 per cent of the US level) (Chart 1). This TFP gap implies that productive efficiency of the Canadian manufacturing sector has deteriorated in the second half of the 1990s relative to that in the United States. The objectives of this article are twofold: first to document the massive widening of the Canada-US labour and total factor productivity gaps in manufacturing over the 1994-2000 period, and second to identify the factors behind this development.4 The article is divided into three main parts. The first section discusses trends in the manufacturing sector in the two countries, including trends in labour productivity, output, employment, capital stock and investment, and the price of labour and capital inputs. The secThe Widening Canada-US Manufacturing Productivity Gap
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".