Estimating price taking behavior with mill-level data: the Norwegian sawlog market, 1974-1991
Bibliographic record
Abstract
A five input transcendental logarithmic (translog) cost function and a set of conditional input demand functions, which were extended to include a conjectural elasticity term, were analyzed. The analysis was based on data covering individual Norwegian sawmills over the period 19741991. The presence of mill-level data allowed us to test cross-sectional effects as well as intertemporal effects. Under the assumption of cost minimization, price-taking behavior was rejected for the years 1982 and 19841991. There was no variation of the conjectural elasticity over regions, but the use of market power increased after the price negotiations were brought from the national to regional levels. The necessity of having information on sawlog purchases and market areas to conclude on welfare effects is explained. This analysis also contributes to explain the post-1992 period, where the Norwegian sawlog market has experienced several structural changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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 teacher head, 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".