Canada-US Softwood Lumber Trade Revisited: Examining the Role of Substitution Bias in the Context of a Spatial Price Equilibrium Framework
Bibliographic record
Abstract
In the last two decades, softwood lumber trade between Canada and the United States has been characterized by numerous trade restrictions. Many studies have attempted to quantify the effects of such sanctions, and in doing so, softwood lumber was modeled as a single, homogenous commodity. However, recent research has suggested that this may be a misleading assumption, since not all softwood lumber products are equivalent substitutes. We refer to this problem as the substitution bias, and uniquely address this issue in estimating the effects of trade restricting policies. Using a spatial price equilibrium (spe) model, impacts of the post-sla import duties are estimated and compared to estimates of two alternative policy regimes – an export tax and quota. By controlling for substitution bias, our estimates indicate a larger share of the tariff burden is placed on us consumers, with Canadian producers suffering less injury compared to estimates using the traditional homogenous lumber assumption. In addition, by comparing the net impact associated with the alternative policy regimes, a policy equivalence result is found. Our results suggest that the short-run impact of a trade restriction is largely independent of the policy regime incorporated, with the collection of quota rents or tax revenues determining overall winners and losers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".