An econometric analysis of output supply and input demand in the Canadian softwood lumber industry
Bibliographic record
Abstract
Few studies have examined the own-price elasticity of Canadian softwood lumber supply or output-adjusted factor demand elasticities over the past two decades, despite the utility of these measures in understanding producer response to tariffs, to market shifts (such as the decline in U.S. public harvest), and to changes in domestic forest policies. The present analysis employs a normalized, restricted quadratic profit function approach to estimate lumber supply and Marshallian factor demand elasticities for three Canadian regions. Results indicate that the lumber supply elasticity in the British Columbia coast region may be twice as large as that in the interior or eastern regions. Comparison of Hicksian factor demand elasticities with earlier studies suggests that the own price elasticity of labor demand may be two or more times larger than that for wood. Results also indicate differential time trends in Marshallian lumber output and wood demand elasticities across regions, rising in the British Columbia coast and falling elsewhere over the past two decades. Morishima elasticities of substitution from the present and past studies indicate that the wood for labor factor intensity is more sensitive to changes in labor price than is the labor for wood intensity to changes in wood price.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".