Economic benefits of the Doha round for The Netherlands; Report submitted to the Ministry of Economic Affairs,
Bibliographic record
Abstract
This study provides insights into the nature and magnitude of the impacts of the WTO Doha Round for international trade and the resulting welfare improvements. The analysis of specific economic consequences for the Netherlands is a special feature of this study. These effects at national level are considered in the context of overall effects at EU and world levels. A third of the estimated benefit is attributable to trade facilitation, a third to agricultural liberalisation and the remaining third to both reductions in industrial tariffs and liberalisation in services. The analysis underlines the importance of trade policy reform by developing countries for achieving the benefits of freer trade. About one quarter of the global gains can only be realized if developing countries actively participate. The results highlight the importance of taking a long-term structural view. Developing countries have to think carefully about the risks of reinforcing an emphasis on primary exports. The Netherlands, could enjoy a disproportionate share of world welfare gains. Dutch output would expand particularly in the food processing and the transport and logistics sectors. The results confirm the comparative advantage of the Netherlands in agriculture, food processing and transport and the large dependence of the Dutch economy on international 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".