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. \nEmerging 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. \nIn 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. \nBrazil 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%. \nStress 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.078 | 0.001 |
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; both teacher heads agree on what is shown here.
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".