Bibliographic record
Abstract
The Uruguay Round Agreement on Agriculture aimed at globally reducing trade distorting subsidies.However measures which were identified as having no or minimal trade distorting effects were to be categorized as Green Box (GB) measures.These measures were exempt from reduction commitments & subsidies under the GB could even be increased without any financial limitations under the WTO.Recent research shows that current GB subsidies do not meet the criterion of ‘no or at most minimal’ trade distorting effects.While there is sufficient theoretical justification for the trade distorting effects of GB, little empirical work has been undertaken to substantiate this claim. Using computable general equilibrium modelling, that is, the Global Trade Analysis Project & Data Envelopment Analysis, this paper shows empirically that GB subsidies do have significant distortive effects on trade & production. The paper shows that reduction of GB subsidies would leave global agricultural output virtually unchanged, registering a marginal increase of 0.13 %. The largest decrease in output is seen in the EU, US, Canada, & Switzerland. Conversely, most developing countries can expect to increase their agricultural output.Even the Least Developed countries would find their agricultural output increase by 3 %. Further, the overall global trade goes down on account of decline in exports of EU, US, Canada etc. Exports of most developing countries register an increase of about 20%.Even LDCs would find their exports go up by 20 %. As global imports decline, most countries would also show a lower dependence on agricultural imports.Agricultural employment would rise in almost all developing countries, while that in developed countries would fall. LDCs could also see their wages and employment go up.Wages would also rise by about 1 % on an average in developing countries. This implies that the poverty attenuating effects of the reduction of GB subsidies would be positive & significant.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 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".