The Canada-U.S. Productivity Puzzle: Regional Evidence of the Pulp and Paper Industry, 1971-2005
Bibliographic record
Abstract
We analyze the total factor productivity (TFP) of the pulp and paper industry in three Canadian provinces (British Columbia, Ontario, and Quebec) and in three U.S. states that are contiguously located south of the border (Washington, Illinois, and Maine) over the period of 1971 to 2005. We find that the industry in the three Canadian provinces had much higher TFP growth rates in the era following the Free Trade Agreement (FTA), signed in 1988. In terms of productivity trend, this relative TFP surge has allowed the industry in the three Canadian provinces to move ahead of Illinois and Washington and closer to Maine which is the U.S. leader in the sample. Our results in this particular case do not support the commonly accepted view that Canada has a productivity problem relative to the U.S. / Nous analysons la productivité totale des facteurs (PTF) de l’industrie des pâtes et papiers de trois provinces (Colombie-Britannique, Ontario et Québec) et de celle de trois états américains contigus au sud de la frontière (Washington, Illinois et Maine) au cours de la période allant de 1971 à 2005. Nous trouvons que l’industrie dans les trois provinces canadiennes a connu des taux de croissance de la productivité totale plus élevés après l’Accord de Libre Échange signé en 1988. En ce qui concerne la tendance de la productivité, cette accélération de la PTF a permis à l’industrie de devancer l’Illinois et Washington et de se rapprocher du Maine qui est le meneur aux E.-U. dans notre échantillon. Dans ce cas particulier, notre analyse ne supporte pas le point de vue souvent exprimé que l’industrie canadienne soufre d’un handicap au chapitre de la productivité totale par rapport à leurs compétiteurs américains.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".