Explaining National Border Effects in the QUAD Food Trade
Bibliographic record
Abstract
Abstract Using a ‘structural’ gravity‐like model, this paper first provides estimates of bilateral ‘border effects’ in food trade among the QUAD countries (the US, Canada, Japan and the EU) at the ISIC (International Standard Industrial Classification) four‐digit level (18 food sectors). It then investigates the underlying reasons for border effect, assessing the role played by policy barriers (tariffs, non‐tariff barriers to trade (NTBs) and domestic support) with respect to barriers unrelated to trade policy, such as information‐related costs, cultural proximity and preferences. In contrast to several previous findings, our results show that policy trade barriers, especially in the form of NTBs, are part of the story in explaining national border effects. Interestingly, in all country pair combinations, NTBs significantly dominate the trade reduction effect induced by tariffs. However, results show that elements linked to information‐related costs and consumer preferences matter a great deal in explaining the magnitude of border effects. These findings have implications for the economic and welfare‐related significance of national borders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".