World Economic Prospects Monthly
Bibliographic record
Abstract
Overview: Global growth in 2019 revised down again ▀ In response to continued weakness in global trade and signs that the softness has spread to other sectors, we have cut our 2019 world GDP growth forecast to 2.5% from 2.7% last month (after 3.0% in 2018). But we see growth accelerating in H2 due to fiscal and monetary policy changes and as some temporary negative forces unwind. While revised fractionally lower, global growth is still expected to tick up to 2.7% in 2020 – but the risks lie to the downside. ▀ The latest tranche of trade data points to another poor quarter in Q1. While the weakness in Chinese trade is partly related to the impact of US tariffs, the causes of the trade slowdown are rather broader. Reflecting this, we have again lowered our world trade growth forecast – we now see it slowing from 4.8% in 2018 to just 2.5% in 2019, only a little above the previous low of about 2% in 2016. ▀ One source of comfort is that the February global services PMI rose to its highest level since November. But retail sales in the advanced economies as a whole have been weak recently and, while consumer confidence bounced in February, it has trended lower over recent months. Reflecting this, we have cut our global consumer spending forecast for this year. ▀ We expect ongoing policy loosening in China and dovish central banks – either in the form of delays to rate hikes and liquidity tightening or via renewed easing – to boost the global economy in H2 and beyond. Some recent temporary drags on growth (such as auto sector weakness) should also wane, providing further modest support. ▀ But the modest rise seen in GDP growth in 2020 exaggerates underlying dynamics due to sharp rebounds in a few crisis‐hit economies such as Turkey, Venezuela and Argentina. And downside risks for 2020 are probably larger than in 2019; benign financial conditions and the weaker US$ assumed in our baseline may not materialise, while the build‐up of debt in EMs could act as a larger‐than‐expected drag on growth.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.416 | 0.464 |
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".