Examination of North American softwood lumber species substitution using discrete choice preferences and disaggregated end-use markets
Bibliographic record
Abstract
Over the past 20 years, four significant and contentious softwood lumber trade disputes have taken place between the United States and Canada. The US International Trade Commission (USITC), relying on aggregate market assessments using elasticity of demand estimation and cointegration methods, has ruled that all North American softwood lumber species are perfectly fungible. The objective of this study is to disaggregate the US softwood lumber market by estimating cross-price elasticity of demand for North American softwood lumber species and species groups in three major end-use markets (floor framing, roof framing, and wall framing products) using a discrete choice preference model. Specifically, this study utilizes a choice-based conjoint model to estimate species and species group preferences, market shares, and price-demand relationships for North American softwood lumber. Research results are compared with published aggregate market cross-price elasticity of demand estimates, such as those relied upon by USITC, to determine whether North American softwood lumber species and species groups are perfectly fungible in the three largest softwood lumber end-use markets. Results demonstrate that distinct differences exist in the substitutability between North American species and species groups of softwood lumber. The results provide notable implications in future USITC trade analyses of the US–Canadian softwood lumber trade issue.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".