The Trade Effect of the EU’s Preference Margins and Non-Tariff Barriers
Bibliographic record
Abstract
Nowadays, trade negotiations afford both liberalism- and protectionism-oriented policies. Indeed, in recent decades, the developed countries have been actively engaged in negotiating many preferential agreements to integrate developing countries (DCs) into world trade and encourage their economic growth, but many of these schemes contrast with the complex rules, often imposed on international markets, that still are an obstacle for exporters. Their presence and related costs reduce the importance of preferential trade agreements (PTAs) in increasing trade flows. This article attempts to assess the impact of preferential trade policies on trade flows controlling for different non-tariff barriers (NTBs), using a structural gravity model. The analysis uses disaggregated data, registered in the year 2017, on EU imports (defined at level HS-6 digit) from a large number of exporters (187 developed and developing countries) and also includes the intra-EU trade. Our results show robust and positive estimates for the impact of preferences on bilateral trade flows, however, higher non-tariff barriers are likely to play a role in reducing both the extensive margins of trade, and so tariff preferences alone are not sufficient to access international markets. The impact of NTBs on the intensive margin of trade is ambiguous; some measures may act as catalysts and therefore increase trade, and others may act as an additional cost of trade and thus hinder trade.
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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 | 0.007 |
| 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 | 0.002 | 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".