Fixed versus floating: vulnerability to rising interest rates
Bibliographic record
Abstract
▀ Looking at different economies' exposure to fixed‐ and floating‐rate private‐sector debt reveals how vulnerable they could be to rising interest rates. Our analysis finds that Hong Kong, Sweden, China and Australia are potentially most exposed via floating rates to rising debt service costs. A 150bp rise in rates would also push several other countries' debt service ratios above the peaks of 2008. Less vulnerable economies include the US and Germany. ▀ High levels of floating‐rate debt imply a large and rapid pass‐through of rising interest rates to firms and households, with negative consequences. Exposure to floating‐rate debt as a share of GDP varies greatly: the highest levels are in Hong Kong, China, Sweden, Australia and Spain, with the lowest levels in the US, France and Germany. ▀ Growing shares of fixed‐rate housing debt in the US, Eurozone and UK mean the impact of higher interest rates may be less severe than a decade ago. Private deleveraging in countries such as the US, UK and Spain could also soften the impact. ▀ A rise of 100bp in short‐term interest rates would raise the debt service ratio after one year by around 2.5% of GDP in Hong Kong, with increases of 1.5–1.7% of GDP in Sweden, China and Australia. The smallest effects would be in the US and Germany. ▀ A 100–150bp rate rise would push debt service ratios in China, Hong Kong, Canada, France and the Netherlands well above their peaks of a decade ago. A similar rate rise would take debt service ratios in Sweden, South Korea and Australia close to, or above, previous peaks. ▀ The distribution of debt within economies, which our analysis does not cover, is also important. For example, there is some evidence that the US corporate sector has a high concentration of debt among borrowers with weak finances. Countries that are highly vulnerable to interest rate rises may see their central banks normalise policy rates more slowly than they otherwise would.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".