The global impact of a sharper US‐China slowdown
Bibliographic record
Abstract
▀ We forecast a moderate global slowdown through 2020, but risks are looming of a sharper downturn in China and the US. If these were to materialise, our simulations suggest global GDP growth would hit a post‐crisis low, with the level of GDP dropping by 0.6% and growth slowing by 0.4 ppt in 2019/20. ▀ Economies with strong trade linkages to China and the US – Korea, Taiwan and Mexico – would suffer most. Conversely, a weaker dollar, lower oil prices and relatively smaller trade flows with the US and China would offset the blow in Europe and for some EMs, including Turkey, Argentina and India. ▀ Since 2010, Chinese activity has been a powerful leading indicator of every major economy's exports, proving stronger than similar indicators for US or eurozone activity. This is even the case for non‐Asian economies such as Canada, Mexico, Italy, Germany, France and the UK. This may reflect deepening trading relationships and the relatively high volatility of Chinese cyclical indicators over the period. ▀ Over the past decade, global macro stability has been supported by the US and Chinese cycles moving counter to each other. But this could reverse if the ongoing Chinese policy stimulus fails to gain traction and the weakness gains momentum.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".