Africa's domestic debt boom: evidence from the African Debt Database
Bibliographic record
Abstract
This paper introduces the African Debt Database (ADD) -a new, comprehensive dataset that traces both domestic and external debt instruments at a granular level. The main innovation is a detailed mapping of Africa's domestic debt markets, drawing on rich, new data extracted from government auction reports and bond prospectuses. The database covers over 50,000 individual government loans and securities issued by 54 African countries between 2000 and 2024, amounting to a total of USD 6.3 trillion in debt. For each instrument, it provides harmonized micro-level information on currency, maturity, interest rates, instrument type, and creditor. The data reveal the growing dominance of domestic debt in Africa -albeit with substantial cross-country variation. Four stylized facts stand out: (i) the rapid expansion of domestic debt markets, especially in middle-income countries; (ii) the wide dispersion in borrowing costs and real interest rates; (iii) large cross-country differences in maturity structures and associated rollover risks; and (iv) a rising debt-service burden, particularly due to international bonds. Generally, this project shows that debt transparency is both feasible and valuable, even in data-scarce environments.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".