Employment Decline in the Douglas-fir Region's Lumber and Plywood Industries: An Analysis of Structural and Cyclical Factors
Bibliographic record
Abstract
Over the years a significant decline in employment had occurred in the Douglas-fir region’s lumber and wood products industry. High levels of unemployment can lead to undesirable economic and social effects. An understanding of the nature of unemployment can facilitate future planning as well as mitigating current problems. This study has attempted to examine the underlying causes of employment decline in the region’s softwood lumber and plywood industries, specifically over the period 1979-86. This time span is of particular importance since there was a rapid decline in employment levels after 1979. There has been much controversy over the causes of this reduction but no comprehensive empirical analysis was ever undertaken to determine its cause. Meanwhile levels of output, which also declined in the early part of this span, have again reached pre-recession levels. A cost function approach was employed as the basis of the empirical analysis. The results suggest that most of the employment decline in these industries has been caused by changes in the structure of production and by increasing labour productivity. Although there are indications of cyclical unemployment, much of the reduction in the industries' labour force seems to be attributable to greater substitution of capital and logs for labour. Simulation analyses tend to suggest that changes in factor prices would not have had any dramatic effect on employment levels. It was found that of the recent employment decline in the two industries, around one-quarter of the loss in the lumber industry and one-third in the plywood industry are caused by cyclical forces. Structural factors were assumed to be the cause of the remaining loss in levels of labour input.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".