Game Theoretic Analyses of the United States-Canada Softwood Lumber Trade
Bibliographic record
Abstract
The ongoing softwood lumber trade dispute between the United States (U.S.) and Canada is one of the most contentious and largest bilateral trade battles over the recent decades. The bilateral trade debate is mostly because of the different forestland ownership systems in the U.S. and Canada, and the alleged timber subsidies by provincial governments to Canadian lumber producers. The Softwood Lumber Agreement (SLA) 2006 between the U.S. and Canada is the latest transitory solution of this dispute, entailing Canada to impose mandatory export charges on its lumber shipments to the U.S. In order to understand this trade dispute from a strategic policy perspective, this study analyzes SLA 2006 using game-theoretic frameworks. A Cournot-Nash duopoly model is developed to assess the possible effects of the export tax on overall lumber price and production of softwood lumber in both countries. The empirical econometric models are estimated to uncover the findings of the Cournot-Nash duopoly model by employing the historical time-series data of the softwood lumber market. Even though the game-theoretic model suggests that the export tax under SLA 2006 could decrease Canadian lumber exports to the U.S., the empirical estimation reveals that SLA 2006 is quite ineffective in restricting the lumber trade between the two countries. This study also develops a two-country two-stage game, and concludes that the optimum export tax under the framework of SLA 2006 is mainly determined by the level of Canadian lumber production costs and the U.S. lumber production capacity. Unlike the actual export tax of 0-15%, the empirical estimation reveals that the monthly optimal export tax ranges from -23% to 30%. Given that SLA 2006 is scheduled to expire in October 2015 and that both countries already started looking for ways forward, this study provides useful information in country-level bargaining and trade negotiations between the two countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".