Playing with blocs: Quantifying decoupling
Bibliographic record
Abstract
We adopt a data-driven approach to measure trade decoupling over 2015-2023. Countries are classified into three groups according to changes in their data-inferred trade costs with the US and China: those shifting toward the US bloc, those shifting toward the China bloc, and those with no change in alignment. We document that while cross-bloc trade costs rose, they were accompanied by falling within-bloc trade costs, with average trade costs falling marginally in line with global trade resilience. We use a quantitative model to compute the real income effects of this reconfiguration of trade costs. Model simulations suggest that real income in the median country in the world, and the median country within each bloc, rose by 0.4-0.6%. Finally, we find a modest amount of bloc misalignment: the median country would be better off switching blocs. These results suggest that trade decoupling may not follow trade-driven economic interests.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".