The international lender of last resort for emerging countries: A bilateral currency swap?
Bibliographic record
Abstract
This study addresses the following puzzle: why did states in the largest emerging economies (EMEs) in Latin America and Asia not use formal institutions to cope with the 2008 crisis? During the 1990s, these economies used and established regional and multilateral monetary arrangements, but in 2008 they turned to ad hoc bilateral swap agreements as a first line of defence. My argument is that to understand this change in monetary responses one needs to consider demand as well as supply factors. Previous studies have predominantly focused on supply factors, i.e. the presence of willing and able international lenders of last resort. However, these studies have neglected the perspective of EMEs in this arrangement. I argue that their preferences were shaped both by past experience (leading to a political stigma against multilateral institutions) and the growing autonomy and economic importance of their central banks. The paper examines a sample of Latin American and Asian countries (Brazil, Mexico, Colombia, Ecuador, South Korea and Indonesia), analysing how the new patterns of monetary cooperation appeared in two phases: 2008 crisis management (which demonstrated a preference for ad hoc currency swaps) and the post-crisis aftermath (which formalized these swaps into regional arrangements based on networks of bilateral currency swaps). The institutional design of international monetary cooperation is changing towards a more fragmented and multi-currency system.
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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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".