Bibliographic record
Abstract
Abstract The rise of China as a major bilateral lender has transformed the financial landscape for developing countries and, consequently, the process of resolving debt crises. We examine how China’s loans impact the response of the International Monetary Fund (IMF) to countries in debt distress. We argue that China’s lending approach and its absence from creditor forums, notably the Paris Club, can complicate the IMF’s efforts in managing debt crises. When China is a major lender, the IMF cannot rely on the Paris Club to coordinate bilateral creditors, and concerns about coordination, free-riding, and borrowers’ outside options can make it more difficult to agree on an IMF program. Therefore, we expect that countries that have borrowed more from China will undergo more protracted negotiations with the IMF in a debt crisis. We test our argument using data on the number of negotiating trips by IMF staff to borrowing countries to prepare IMF loans from 2000 to 2019. We find that countries with higher levels of outstanding debt to China require a greater number of IMF negotiating trips if they are in debt distress at the time. Our findings highlight the impact of Chinese lending on the sovereign debt regime and contribute to debates about China’s engagement with multilateralism.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".