The Impact of Regulatory Heterogeneity on Agri‐food Trade
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We estimate the impact of regulatory heterogeneity on agri‐food trade using a gravity analysis that relies on detailed data on non‐tariff measures (NTMs) collected by the NTM‐Impact project. The data cover a broad range of import requirements for agricultural and food products for the EU and nine of its major trade partners. We find that trade is significantly reduced when importing countries have stricter maximum residue limits (MRLs) for plant products than exporting countries. For most other measures, due to their qualitative nature, we were unable to infer whether the importer has stricter standards relative to the exporter, and we do not find a robust relationship between these measures and trade. Our findings suggest that, at least for some import standards, harmonising regulations will increase trade. We also conclude that tariff reductions remain an effective means to increase trade even when NTMs abound.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it