Incidence of the 1996 Canada–U.S. Softwood Lumber Agreement and the Optimal Export Tax
Bibliographic record
Abstract
Our partial‐equilibrium analysis suggests 63% of the Canada‐U.S. Softwood Lumber Agreement's export tax is absorbed by Canadian consumers. Still, sufficient surplus was extracted from U.S. consumers for the agreement to be in Canada's national interest. In fact, the agreement was suboptimal from a Canadian perspective in that a higher tax rate would have raised national welfare, at least in the short run. Although the agreement decreased U.S. welfare, the net loss for the combined U.S. and Canadian economies is modest, about 5% of the bilateral softwood lumber trade value according to our baseline estimates. This suggests the agreement's tariff rate quota scheme is a reasonably efficient mechanism for redistributing economic surplus from U.S. consumers to producers. Still, a better policy may be to enlarge the softwood lumber market via a research and promotion program funded by a modest (say, 5%) tax on Canadian exports.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".