The European Union preferential trade with developing countries. Total trade restrictiveness and the case of sugar
Bibliographic record
Abstract
In order to assess the extent to which developing countries receive preferential treatment from the EU, Conforti, Ford, Hallam, Rapsomanikis and Salvatici use a mercantilist trade restrictiveness index (MTRI). They demonstrate that although Least Developed Countries (LDCs) face a relatively low level of protection across all sectors, even before the implementation of the EU’s Everything but Arms (EBA) initiative, many developing countries are highly constrained in their trade with the EU. These developing countries include some of the more competitive developing countries such as Brazil and Argentina, but also the African, Caribbean and Pacific (ACP) non-LDC group. In agricultural trade, LDCs do not appear to be provided a high degree of preference by the EU, as they seem to face higher MTRI indices than other more developed countries such as Chile and Canada. Taking the issue of ACP non - LDC countries further, the paper investigates in more detail trade in sugar, a product where these countries face high protection at the margin. By using a global partial equilibrium model for the sugar market and a gravity model to replicate LDCs bilateral trade with the EU, to simulate EU sugar market reform proposals, they suggest that trade will be diverted from countries currently enjoying preferential access, particularly higher cost ACP countries exporting within the sugar protocol, and will be displaced by more efficient LDC producers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".