The great rotation in global credit risks
Bibliographic record
Abstract
▀ The pattern of global credit risks looks very different today than in 2007. Risks are now mostly centred in China and emerging markets. “Excess” private debt in China is as high as $3 trillion compared with $1.7 trillion in the US a decade ago. Yet some pockets of significant risk still exist in advanced economies, which not only implies vulnerability to rising interest rates, but also that the scope for rate rises may be limited. ▀ With policy normalisation underway in the US and the scaling back of asset purchases expected to start soon in the Eurozone, we focus on assessing vulnerabilities across global credit markets. This article explores the topic using a top‐down, cross‐country approach. We find that although private debt and debt service ratios look more benign in advanced economies than a decade ago, they have deteriorated markedly in many emerging markets in recent years. ▀ Based on a measure of excess private debt – comparing private credit‐to‐GDP ratios with their trend – China, Hong Kong and Canada are the riskiest. When comparing debt service ratios relative to their long‐term averages, risks are also mainly concentrated in emerging countries. But Canada, Australia and some smaller European countries also have high debt service ratios that have failed to drop since 2007, despite the slump in global interest rates. ▀ Overall, aggregate private debt indicators look less worrying than in 2007. We would also argue that the concentration of excess private debt levels in China reduces the risk of a sudden financial crisis based on massive credit losses, such as the one in 2007–2010. But with corporate debt levels in the US, Canada and some other G7 countries above their long‐term trend, investors need to be attentive to these considerable pockets of risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".