Estimating the Impacts of Government Interventions in the International Rice Market
Bibliographic record
Abstract
A procedure is developed to estimate the distortions and welfare impacts of government interventions in the international rice market. The procedure is based on a spatial equilibrium model and the application of conjectural variations. The model estimates asymmetric price distortions caused by policies in both exporting and importing countries. We find that the measures of price distortion are slightly larger due to actions in importing countries than in exporting countries. The results show that welfare gains of U.S.$1.2 billion or about 14.8% are possible when all trading distortions are removed. Nous avons mis au point une méthode pour estimer les répercussions de l'intervention gouvernementale sur les distorsions et le bien‐être sur le marché international du riz. Cette méthode est fondée sur un modèle d'équilibre spatial et l'application de variations conjecturales. Le modèle estime les distorsions de prix asymétriques causées par les politiques des pays exportateurs et importateurs. Nous avons trouvé que la distorsion des prix est légèrement plus élevée dans les pays importateurs que dans les pays exportateurs, en raison de leurs mesures respectives. Les résultats ont montré qu'il était possible de réaliser des gains de 1,2 milliard $US, soit environ 14.8 %, en éliminant la distorsions des échanges.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".