Technical Progress and Sustainable Growth in the Manufacturing Sector of North American Countries, 1984–2022: A Stochastic Frontier Analysis
Bibliographic record
Abstract
This article presents an estimation of a stochastic frontier model using a translogarithmic production function to identify the impact of production factors—labor and capital—along with CO2 emissions and technical progress on the value added of the manufacturing sector in North American countries over the 1984–2022 period. The model also provides estimates for technical efficiency, scale efficiency, and technological change, allowing for a comparative analysis of these indicators’ evolution within the manufacturing sectors of Canada, Mexico, and the United States. The findings indicate that capital exerts the strongest influence on manufacturing value added, followed by labor. CO2 emissions exhibit the anticipated negative effect on the sector’s value added. Notably, the average technical efficiency of Mexico’s manufacturing sector is higher than that of Canada and the United States over the studied period. Regarding technological change, the United States demonstrates the highest values, followed by Canada, with both nations displaying an upward trend throughout the years, while Mexico shows a declining trend in this indicator.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".