The Impact of Export Price Volatility on Market Behaviour in the International Export Market: A Case Study of Canadian and German Pork Exports in China
Bibliographic record
Abstract
This study delves into the intricate dynamics of export price volatility and its impact on the market behaviour of major pork exporters, Germany and Canada, in the world’s largest pork market, China. Exporters’ market behaviour often responds to the uncertainty arising from price fluctuations by curtailing their supply; a reduction in the supply by a major exporter can disrupt the overall market supply, potentially leading to an increase in prices. However, the extent to which an exporter can leverage this increase in price depends on the responsiveness of demand to such changes. To explore these relationships, a residual demand function elasticity model (RDE) is extended to incorporate price volatilities. Prices and their volatilities are modelled using Autoregressive and GARCH models, respectively. The results of the RDE analysis of pork exports reveals strong competition among pork exporters in the Chinese pork market and indicate that price volatility affects the market power of the exporting country. This research not only contributes to the understanding of the interplay between export price volatility and market power but also provides practical insights for major pork exporters. This study helps formulate informed strategies to navigate the challenges posed by price fluctuations in the international pork market.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".