Soaring corporate debt is a risk to global growth
Bibliographic record
Abstract
▀ Corporate borrowing is accelerating as a result of the coronavirus crisis. In part, this is a healthy development as firms look to ride out a period of low or even zero sales. But it also brings potential risks to growth, especially in the longer term, including via lengthy balance sheet restructuring that hurts investment and productivity growth. ▀ In the advanced economies, we estimate the aggregate corporate debt/GDP ratio could rise as much as 10ppts in 2020, to 95% of GDP ‐ well above the 2009 peak. Debt service ratios may also rise into risky territory despite low interest rates. Risks look especially elevated in France and Canada. ▀ Evidence for both advanced and emerging economies suggests high corporate debt levels can damage growth. Highly indebted firms tend to invest less in both the near and medium terms, and some estimates suggest the rise in aggregate debt this year could cut GDP growth by up to 0.2% per year. ▀ The coronavirus crisis may also crystallise some pre‐existing risks in corporate debt. Despite government assistance, defaults by low‐rated firms have started to rise and commercial real estate prices are falling. ▀ Sectoral concentrations of risk may also be intensified and new ones created in industries hit hard by the virus like energy and consumer discretionary sectors. ▀ Emerging market corporate debt is also on the rise ‐ sharply in some cases. In some economies, this mostly reflects exchange rate effects. But negative balance sheet effects of this kind are also a risk to growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".