Currency compositions of international reserves - recent developments
Bibliographic record
Abstract
This policy brief presents a new comprehensive dataset on the currency compositions of international reserves of 64 economies from 1996 to 2023. The dataset contains country-specific shares in international reserves for the eight major international currencies, i.e. the United States Dollar (USD), the Euro (EUR), the Japanese Yen (JPY), the British Pound (GBP), the Canadian Dollar (CAD), the Australian Dollar (AUD), the Chinese Yuan or Renminbi (CNY), and the Swiss Franc (CHF). The dataset provides an up-to-date and comprehensive account of publicly available data on the denomination of international reserves, including data on international currencies other than the USD, EUR, JPY, and GBP. While the USD and the EUR remain the dominant global reserve currencies, the data indicate their significance has diminished as countries diversify their reserves. Currencies, including the CNY, have gained prominence, hinting at a gradual fragmentation of the international monetary system. While the USD should retain its leading role in the medium term, ongoing geoeconomic shifts suggest a move towards a multipolar reserve currency landscape. The eventual look of this landscape will depend on the credibility of reserve currency candidates and their ability to retain the characteristics that make them desirable as reserve currencies in the face of future economic and political developments.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.014 |
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".