Do state‐owned enterprises benefit more from China's cereal grain tariff‐rate quota regime?
Bibliographic record
Abstract
Abstract In 2016, the United States launched a formal dispute with the World Trade Organization (WTO) concerning China's wheat, corn, and rice tariff‐rate quota (TRQs) administration. A formal panel was requested in August 2017, with several major grain exporters, including Canada, joining as third‐party members. This study employs two unique micro‐level datasets to investigate the role of state‐owned and non‐state‐owned enterprises’ (SOE and non‐SOE, respectively) in China's agricultural imports. Results suggest that SOEs are noticeably more active in importing quota‐bound commodities compared to quota‐free imported commodities. Moreover, the larger role of SOEs in China's cereal grain imports is negatively correlated with China's food security targets, as measured by estimated prior year stocks‐to‐use ratios. Conversely, above average food security targets in China's cereal grain market leads to an important extensive margin adjustment of non‐SOE import participation. Finally, we find very little compelling evidence that China's September reallocation of unused TRQ has any economic or statistically significant impact on non‐SOE entry into importing or the intensity with which their imports occur.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".