Exchange-rate fluctuation and pricing behavior in China’s wood-based panel exporters: evidence from panel data
Bibliographic record
Abstract
As the largest wood-based panel producing and exporting country, China has experienced sharp reductions in the growth rate of its wood-based panel exports because of pressure caused by exchange-rate fluctuation. These fluctuations were exacerbated by the global financial crisis. China’s wood-based panel exporters need to adjust their pricing strategy to cope with the changes of international market conditions. Thus, it is necessary to investigate the relationship between exchange-rate fluctuation and the pricing behavior of China’s wood-based panel exporters. To evaluate the impact of the exchange rate on China’s wood-based panel export prices across multiple destinations, a pricing-to-market model incorporating panel data was used. The empirical results suggest that although complete exchange-rate pass-through exists widely in China’s wood-based exports, China’s fiberboard and plywood exporters tended to adopt different pricing strategies in the international wood-based panel market during the post-crisis era. China’s fiberboard exporters often used the pricing-to-market model to determine prices in the main export destination countries, while China’s plywood exporters tended to amplify the exchange-rate effects. This indicates that China’s plywood exporters have stronger international market power than China’s fiberboard exporters, partly because China’s plywood exporters have more advantages in terms of skilled labor.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".