Sunshine over Shanghai: Can the WTO Illuminate the Murky World of Chinese SOEs?
Bibliographic record
Abstract
Abstract State-owned enterprises (SOEs) are a major force in the Chinese economy and a growing presence in international trade and investment. The challenge to the WTO legal regime is commercial, given the size of SOEs and their share of Chinese output, and political, given worries that trade and investment by SOEs may be driven by public policy goals. And both challenges may be exacerbated by the murky world of Chinese SOEs. In this article, I first review whether Chinese SOEs are a problem for the WTO, and whether more sunshine on their operations might be a useful discipline. I then ask what we know about SOEs inside the WTO, including in the Trade Policy Review Mechanism. Since the answer is, not much, I consider whether mega-regional trade negotiations offer a better approach. My answer being negative, I finally consider whether an attempt to negotiate a WTO Reference Paper on SOEs might help. I conclude that transparency is likely to be a better discipline on the spillovers associated with SOEs than a search for binding rules, while also helping everyone better understand the efficiency effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".