Bibliographic record
Abstract
Purpose The purpose of this paper is to apply a stochastic Frontier production model to Canadian manufacturing industries, to investigate the sources of total factor productivity (TFP) growth. As productivity (growth) appears to be the single most important determinant of a nation's living standard or its level of real income over long periods of time, it is important to better understand the sources of productivity growth. In Canada, TFP growth is the major contributing factor (relative to changes in capital intensity) to labour productivity growth, particularly in manufacturing sector. However, the TFP gap is also the main source of labour productivity gap between Canada and other industrialized (Organization for Economic Co‐operation and Development) countries in recent years. Design/methodology/approach In this paper, a stochastic Frontier production model is applied to Canadian manufacturing industries to investigate the sources of TFP growth. Using a comprehensive panel data set of 18 industries over the period 1990‐2005 and the approach proposed by Kumbhakar et al. and Kumbhakar and Lovell, TFP growth is decomposed into technological progress (TP), changes in technical efficiency, changes in allocative efficiency and scale effects. Findings The decomposition reveals that during the period under study, TP has been the main driving force of productivity growth, while negative efficiency changes observed in certain industries have contributed to reduce average productivity growth. In addition, the empirical results show that research and development expenditure, information and communications technology investment, as well as trade openness exert a positive impact on productivity growth through the channel of efficiency gains. Originality/value The author argues that the decomposition carried out in this study may be very helpful to elicit the correct diagnosis of Canada's productivity problem and develop effective policies to reverse the situation, thereby reducing Canada's lagging productivity gap.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".