Recovery In Trade Between The Edb Countries
Bibliographic record
Abstract
The stabilisation of commodity prices and the resultant stabilisation of national currencies in most EDB countries in 1Q 2017 create a favourable basis for a recovery in trade in the EDB region. Suffice it to note that all EDB countries showed growth in mutual trade turnover in 1Q of the year; notably, the highest turnover growth rates were observed in Kazakhstan (nearly 41% y-o-y) and the Russian Federation (33.7% y-o-y). The situation with the countries’ mutual ties also improved as regards migrants’ remittances: growth in remittances to Kyrgyzstan exceeded 54% in 1Q 2017, while in Armenia this indicator was 14% y-o-y in 1Q 2017. In this review, our special report focuses on macroeconomic ties among the EDB countries and the economic growth transmission channels, mainly as regards foreign trade and remittance flows. Our study testifies to a considerable role of these factors in the regional countries’ economic recovery, but we also note that the region’s economies remain highly vulnerable to external shocks, and to energy price fluctuations above all. Russia’s economic growth outlook for 2017 has improved from 0.8% to 1.3%. The projection change was influenced by the first quarter’s economic data and a revised oil price projection that took into account the first quarter’s trends in world energy prices. The improvement in the Belarusian GDP projection from minus 0.5% to positive 1.3% over 2017 is caused by higher than expected economic activity recovery rates in the 1st quarter and by the agreement reached on gas prices and oil supply from the Russian Federation. Kazakhstan’s improved GDP outlook is caused by stronger external demand and by the expansion of the State budget deficit with a view to revitalising the banking system. More optimistic assumptions concerning the foreign economic situation and a revised fiscal boost were the main factors behind the improved GDP outlook for Kyrgyzstan. Tajikistan’s economic growth projection was revised downwards as its banking sector situation deteriorated. A key risk for the remainder of 2017 is renewed volatility in the commodity markets after a period of strengthening exchange rates of some EDB countries’ currencies. Early May developments in both the Russian and Kazakh financial markets showed that, after a considerable strengthening in the preceding months, the national currencies have become more sensitive to increased volatility in oil prices. We expect that, given the increased external risks, the CB of the Russian Federation may harden its rhetoric and slow the reduction in the key rate.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".