Assessment of Russian embargo impact on economies of the EU countries : an input-output approach
Bibliographic record
Abstract
The purpose of this study is to quantify the impact of Russia’s embargo on the economies of most affected EU countries. Russia is the fourth largest trading partner and the second largest importer of Europe’s agriculture products. According to the Eurostat, Russia’s food import counts approximately 10% of Europe’s total export of agriculture and food products. In June 2014, the European Union (EU) adopted a series of economic sanctions against Russia due to the Ukraine’s territorial crisis. As retaliation, Russia imposed a one-year food embargo on the import of a whole range of food products from the EU, Norway, Australia, Canada and the USA on 7 August 2014. In June 2015 the ban was extended to be effective until August 5, 2016, and it may be subsequently extended for another 1-year period. The most affected European countries are: the Baltic States, Finland, Poland, and Germany (as shown in the database of GTAP 2011). The impact of Russia’s counter-sanction on the economy of the EU countries is assessed in this study by conducting Input-Output multiplier analysis together with comparison studies. In order to allow a holistic view of the impact on the interested regions, the disaggregated Input-Output matrix for those four European countries of interest is constructed from the dataset of the Global Trade Analysis Project (GTAP) in 2011. The results show that the impact on the whole economy of these four countries is moderate in terms of their change in GDP, but it does have significant negative impacts on certain industries of each economy, for instance, bovine meat industry in Germany, vegetables and fruits in both Baltic States and Poland, and dairy products in Finland. These impacts on production level may further forward its negative effects to the related labors and firms who run the risk of losing their income due to the embargo.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".