Softwood lumber trade and trade restrictions: gravity model.
Bibliographic record
Abstract
Abstract A gravity trade model can be used to determine the effects of policy on bilateral trade flows. The gravity model is initially explained and then used to determine the effect that U.S. tariffs have on softwood lumber (SWL) imports from Canada, using information from the 2006 Softwood Lumber Agreement. Quarterly data for seven Canadian and three U.S. regions for the period 2007-2017 are used to estimate a gravity model of SWL trade. The model is subsequently expanded to include Japan and China as separate regions, and then as a combined China-Japan region. The model is estimated using OLS and a Poisson Pseudo-Maximum-Likelihood method for trade quantity and value. Findings indicate that: (1) the imposition of a countervailing and/or anti-dumping duty usually has a negative effect on Canada's physical exports, but not in all cases; (2) the value of softwood lumber trade decreases by 26% on average under a tax/tariff compared with no duties; (3) the tax/tariff has a smaller but still significant impact on Canadian exports when China and Japan are included, as SWL exports are diverted from the U.S.; and, not surprisingly, (4) duties affect the value of lumber exports to a much greater extent than quantity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".