World Economic Prospects Monthly
Bibliographic record
Abstract
Overview: Coronavirus to cut global growth to new lows ▀ The rapid spread of coronavirus will weaken China's GDP growth sharply in the short term, causing disruption for the rest of the world. We now expect global GDP growth to slow to just 1.9% y/y in Q1 this year and have lowered our forecast for 2020 as a whole from 2.5% to 2.3%, down from 2.6% in 2019. ▀ Prior to the coronavirus outbreak, there had been signs that the worst was over for both world trade and the manufacturing sector. However, this tentative optimism has been dashed by the current disruption. ▀ While the near‐term impact of the virus is uncertain, the disruption to China will clearly be significant in Q1 – we expect Chinese GDP growth to plunge to just 3.8% y/y. Even though growth there will rebound in Q2 and Q3, it will take time for the loss in activity to be fully recovered and we now expect GDP growth of just 5.4% for 2020 as a whole, a downward revision of 0.6pp from last month. ▀ Weaker Chinese imports and tourism and disruption to global supply chains will take a toll on the rest of the world, particularly in the Asia‐Pacific region. And the shock will exacerbate the ongoing slowdown in the US and may result in the eurozone barely expanding for a second quarter running in Q1. ▀ Weaker oil demand in the short term has prompted us to lower our Brent oil price forecast. We have cut our projection for growth in crude demand in 2020 by 0.2m b/d to 0.9 mb/d and now forecast Brent crude will average $62.4pb in 2020, down from about $65pb in our January forecast. ▀ Quarterly global growth is likely to strengthen a little in H2 this year as the disruption fades and firms make up for the lost output earlier in the year and the effect of China's policy response starts to feed through. But for 2020 overall, global growth is now likely to be just 2.3%, 0.2pp weaker than previously assumed as a result of the epidemic.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.341 | 0.327 |
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".