Measuring the Export Subsidy Equivalents (ESEs) through Price Discrimination Generated by Exporting State Trading Enterprises
Bibliographic record
Abstract
The Doha Round framework agreements state that all forms of export subsidies should be eliminated, which includes not only export subsidies through food aid and export credits, but also "consumer financed" ones through exporting STEs. Therefore, one needs a theoretical definition and practical measurements of "hidden" export subsidies unregulated under the current WTO rules. This paper proposes a basic definition for the "consumer financed" export subsidy equivalent (ESE) created by STEs' price discrimination among export markets as well as price discrimination between export and domestic markets. Examples of calculated ESE values are shown using the Canadian dairy STE with price discrimination between export and domestic markets and the Australian wheat STE wlth price discrimination among export markets. The ESE proposed here would provide a useful measurement of "consumer financed" export subsidies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".