A Gravity Model of Softwood Lumber Trade: An Application to the Canada-U.S. Trade Dispute
Bibliographic record
Abstract
A gravity model of softwood lumber (SWL) trade is developed and used to determine the effect that U.S. tariffs have on SWL exports from Canada to the U.S. The gravity model employs quarterly data for seven Canadian and three U.S. regions over the period 2007–2019; it is expanded to include Japan and China as separate regions, and then as a combined China-Japan region. The effect of a Canadian export tax or U.S. import tariff is examined using information on the Softwood Lumber Agreement (effective from 2006 to 2015), which included a trigger mechanism that varied the tax/tariff. The gravity model was estimated for trade quantity and value using OLS and a Poisson Pseudo-Maximum-Likelihood estimation method for different configurations of the China-Japan export regions. Our findings indicate that (1) the imposition of a countervail 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 0.054% on average with each 1% increase of tax/tariff; (3) the tax/tariff has a significant impact on Canadian exports when China and Japan are included; 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".