The Impact of Economic Sanctions on Russia and its Six Greatest European Trade Partners
Bibliographic record
Abstract
The Ukrainian crisis of November 2013 has led to the proclamation of independence of the Republic of Crimea in March 2014, and its attachment to Russia. This attachment, recognised by Russia and contested by a large number of Western countries, triggered an international crisis between the Russian Federation and the West (European Union, United States of America, et cetera). As a means of applying pressure on Russia, Western countries decided to launch a set of international sanctions. This paper's goal is to assess on sanctions effects on Russian and European economies. Thus, a country structural vector autoregressive (CSVAR) model is used in order to witness the impact of a sanction shock on considered economies. To our best knowledge, this paper is the first to use a CSVAR model to study the economic growth effects of anti-Russian sanctions on the considered economies. The economic conflict repercussions are revealed on the Euro Area (19 countries), on the six biggest trade partners of Russia as a lone entity, and finally on the six biggest trade partners of Russia separately. Results witness that the shock's effects are quite different whether a sum of GDP is used or not. In addition, results reveal that Russia is the most impacted by sanctions with a quarter-on-quarter GDP growth decrease of 3.25% after 3 quarters. Yet, European economies are also negatively impacted by sanctions, even if the impact is much weaker: -0.075% for Finland, -0.025% for France, -0.0125% for Germany, -0.012% for Italy, and -0.063% for Poland. As a consequence, we can say that the own coercive measures of European countries have a negative impact on their economies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".