Data envelopment analysis of technical efficiency and productivity growth in the US Pacific Northwest sawmill industry
Bibliographic record
Abstract
This paper uses data envelopment analysis (DEA) to characterize the changing production frontier (technical efficiency, productivity growth, technical and efficiency change, and returns to scale) of the sawmilling industry in the Pacific Northwest (PNW) US using geographical panel data for the period 1968–2002. Unlike past DEA studies, we develop confidence intervals for all estimates using an improved bootstrapping method. The results indicate that the gap between the least and most efficient regions in PNW has grown and the least efficient regions are falling further behind the most efficient regions. For the Oregon regions, the null hypothesis of constant returns to scale (CRS) could not be rejected for any year. For the Washington regions, returns to scale varied year by year, although only two of the five regions showed strong tendencies away from CRS. For PNW as a whole, mean productivity growth was 0.5% per year between 1968 and 1992. Between 1992 and 2002, the regional mean was 1.3%, although with wide variation across regions. DEA results indicate that the vast majority of productivity growth in the PNW sawmilling industry between 1968 and 2002 was due to technical change. Improvements in scale efficiency played a very small role, and efficiency change was zero or negative.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".