China's Economic Statecraft in Latin America: Evidence from China's Policy Banks
Bibliographic record
Abstract
Most scholars and policy makers classify the motivation behind China’s global economic activity as an effort to project soft power or to exercise “extractive diplomacy” by locking up natural resources across the globe. In this paper we argue that China, through its state financial institutions and firms, is also significantly motivated by simply commercial reasons. To shed light on this debate, we examine the extent to which China’s policy banks provide finance to sovereign governments in Latin America. We find that Chinese policy banks now provide more finance to Isatin American governments each year than do the World Bank and Inter-American Development Bank (IDB). Indeed, the large loan size, high interest rates and focus on industry and infrastructure of Chinese finance has less in common with these international financial institutions (IFIs) and more in common with the private sovereign bond market. In this way, Chinese finance appears primarily commercial in nature. Chinese banks offer slightly lower interest rates than the private market, but these arc not necessarily concessional subsidies to support a political agenda. The Chinese banks are exposed to less risk because they tic their loans to equipment purchase requirements and oil purchase contracts. Through these risk-lowering arrangements, Chinese banks can profit by lending to countries that have been priced out of the sovereign debt market. While it can be difficult to distinguish between the three types of economic statecraft outlined above, we argue that commercial profit is also a major force behind China’s economic statecraft that has been largely overlooked.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".