World Economic Prospects Monthly
Bibliographic record
Abstract
Overview: Global GDP growth to fall to a post‐crisis low ▀ The latest flare‐up in US‐China trade tensions and ongoing industrial weakness are adding to concerns about the global economic outlook. As a result, we have again lowered our world GDP growth forecasts slightly, to 2.6% in 2019 – the weakest since the global financial crisis – and then 2.7% in 2020, both well below the average of 3.0% in the preceding five years. ▀ In July, the global manufacturing PMI remained below the 50 no‐change level for the third month running and survey‐based measures of firms’ export orders continued their downward trend. Q3 will probably be another weak quarter for trade and industry. ▀ Japan's trade restrictions on Korea highlight the risk of trade tensions creeping beyond the US‐China frontline. Accordingly, we have scaled back both our world trade and investment forecasts. Both are now expected to record very weak rises in 2019 and then stage only modest recoveries in 2020. ▀ Although the recent softening in service sector surveys points to manufacturing woes spilling over to the more domestically‐focused sectors of the economy, consumer indicators overall have been fairly resilient. Our advanced economy consumer confidence indicator climbed to a 2019 high in July, while retail sales were strong at the end of Q2. We expect solid household spending to offset weakness elsewhere and to limit the scale of the slowdown in global growth. ▀ A clear downside risk to our forecast is that the US carries out its threat to impose 10% tariffs on a further $300bn of imports from China. The intention may be to railroad Beijing into agreeing a deal, but the persistent moving of the goal posts may make it disinclined to negotiate. We estimate that the tariff hikes could lower global GDP growth by just 0.1pp in 2020, but the impact could be larger if it has substantial confidence effects or leads to a significant tightening in financial conditions.
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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 | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.406 | 0.432 |
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".