Sovereign default history: evidence of supranationals' preferred creditor status
Bibliographic record
Abstract
Low Default Rate to Supranationals: Fitch Ratings’ study of sovereign defaults to different groups of creditors (bilateral official, private, and supranational) in 1999-2018 shows that most defaults to Fitch-rated supranationals are accompanied by defaults to other groups of creditors. Our annual observations show that there have been only three cases of default to multilateral development banks (MDBs) not accompanied by a default to another official or private creditor: Iran (2013), Syria (2002) and Yugoslavia (until 2003). For the survey we used the Bank of Canada database and our own default statistics on MDBs. Joint Default Rates: Scenarios involving a joint default to a supranational and another official creditor are more frequent, while there was no joint default to both a supranational and private creditor. This indicates that countries defaulting to supranationals are in most cases ones with limited or no access to capital markets, and relying largely on assistance from public development agencies. Evidence of Preferred Creditor Status: These statistics provide evidence of MBDs’ preferred creditor status (PCS). PCS is a widely accepted principle under which MDBs are given priority for repayment of debt in the event of a sovereign borrower experiencing financial stress. The servicing of MDBs’ non-sovereign loans is also protected against restrictions on foreign exchange.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".