Interregional Competition in the Wood Products Industry: An Econometric Spatial Equilibrium Approach
Bibliographic record
Abstract
This study presents a multiregional model of the soft wood forest products industry in the United States, designed to describe the dynamics of interregional competition in the industry and to provide means for policy experimentation and short-term projection of regional market shares. Two products (softwood lumber and plywood), five product supply regions (including Canada), and six product demand regions are recognized. The design of the model is based on a combined top-down/bottom-up approach and consists of three interdependent components: (1) the aggregate product market, (2) regional product markets, and (3) regional factor markets. Model solutions are obtained by the simultaneous determination of national level product prices and quantities and allocation of equilibrium quantities across producing regions on the basis of their relative prices and locational advantage. The model is evaluated in an historical simulation using data for 1950-84. Graphical analysis of simulated series suggests that the model replicates short-run trends as well as cyclical movements in aggregate demand and regional market shares. The results indicate that the short-run impacts of relative prices and locational advantage on regional market shares are generally small. Price responsiveness of regional market shares for lumber appear to be considerably lower than that of plywood, indicating greater degrees of regional substitution in the plywood market. The forecasting application of the model is demonstrated by extrapolating the complete structure for two years beyond the sample period. The projected trend during this two-year period is one of increasing demand for both lumber and plywood. Domestic producers' shares of the lumber market are expected to remain relatively stable. The results show that nearly all increases in demand for lumber in this period will be satisfied by Canadian imports.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".