Examining the Canada–China agri‐food trade relationship: Firms, trading partners, and trading volumes
Bibliographic record
Abstract
Abstract The “new‐new” trade theory has dramatically shifted the focus of international economics research to heterogeneous firms and the margins by which firms participate in international trade. However, few studies have examined the dynamics of agricultural trade at the firm level. This paper employs China Customs data comprising the universe of Chinese firm‐level agricultural import transactions over the period 2000–2009 and develops an empirical strategy to decompose the growth of Chinese agri‐food imports of its four major suppliers, Canada, the United States, Brazil, and Argentina. Our findings reveal that China's growth in agricultural imports is highly concentrated among a small group of firms, where the top 10% of Chinese agricultural importers account for nearly 90% of its agricultural imports. We also find evidence of a significant turn‐over of importers of agri‐food products. Over 40% of new firms entering China's agricultural import market during our sample exited after just 1.7 years. Finally, decomposing import growth patterns for Canada and its competing suppliers reveals significant differences in the intensive and extensive margins of trade that hold important implications for trade policies aimed at enhancing Canada's position as a major agri‐food supplier in the Chinese 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".