Are State-Contingent Sovereign Bonds the Solution to Avoid Government Debt Crisis?
Bibliographic record
Abstract
The idea that sovereign borrowers may issue new debt, the service of which is contingent or GDP growth (GDP linked bonds) has been increasingly discussed in recent years. Some central banks (England, Canada and, recently, Germany and France) have taken steps to raise the awareness of stakeholders and launch a global conversation on GDP-linked bonds. The IMF participated in this debate though with extreme caution. The G20 mentioned the issue in its last Hamburg communiqué but refrained from taking side. GDP-linked bonds offer many advantages. They would limit the issuers’ debt-service obligations in time of slow or negative growth, reduce the likelihood of debt crises and defaults, avoid sharp spending cuts in order to maintain access to capital markets, and even provide some latitude for additional spending at a time when it is most needed. GDP-linked bonds would also render investors more responsible when it comes to lending money to a sovereign. In addition, investors would know in advance the terms of their bond restructuring and gain an equity-like exposure to a country. The counter-cyclical feature of GDP-linked bonds and the fact that they would alleviate the economic cost of a debt restructuring would also make them beneficial for financial stability and the broader economy. These benefits would justify a global policy initiative to promote the idea and kickstart the market. However, many issues remain unresolved (pricing, design, institutional framework…). The learning curve for such a new financial product might, therefore, justify a cautious and experimental approach even though the quick development of a large GDP-linked bond market would have many advantages, including liquidity and arbitrage.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".