Foreign Exchange Reserves. Recent Evolution
Bibliographic record
Abstract
The world foreign exchange reserves declined 7% from the first quarter of 2014 to the second quarter of 2016, mostly due to a significant reduction in reserve accumulation in emerging and developing economies – a 13% drop – which compares with a slight increase of 4.5% in advanced economies in the same period. Emerging and developing economies reduced their share of world reserves as a result – from 68% in the second quarter of 2014 to 64% in the second quarter of 2016 – whereas the advanced economies’ share rose from 32% to 36% in the same period. The increase in foreign exchange reserves in developed countries was not only in relative but also in absolute terms. In Latin America and the Caribbean, there were two different trends in the accumulation of foreign exchanges reserves in the period – a downward trend since 2014 up to the end of 2015, and an upward trend for the first eight months of 2016. The behavior of nominal exchange rates in the region (based on a combined index of currencies) was also characterized by two different trends in the same period – a strong depreciation since 2014 up to the end of 2015 and a slight appreciation in the first eight months of 2016. Brazil has maintained the largest foreign exchange reserves in the region. Brazilian reserves increased US$ 1.4 billion from August 2015 to August 2016. Mexico has the second largest reserves in the region. It has kept its position despite a reduction in its reserves of almost US$ 10 billion in the same period. Peru has the highest reserves-to-GDP ratio in the region (32%), followed by Uruguay (28%), while Argentina has the smallest (5%). The region’s average reserves-to-GDP ratio is 17%. Stress testing the stockpile of foreign exchange reserves in the region show that reserves for most countries are enough to finance their current account deficit for more than two years and at least three quarters of imports.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".