The economic impact of the Russian import ban: A CGE analysis
Bibliographic record
Abstract
The aim of this paper is to assess the economic impact of the Russian embargo from 7 August 2014 on certain agricultural food products from the EU, the USA, Norway, Canada and Australia. The effects of this economic sanction are analysed in the framework of a computable general equilibrium (CGE) model with a particular focus on bilateral and total exports, production and welfare. The detailed, based on real trade data, calibration of the model allows for an exact identification of the sectoral shares and prohibitive tariffs aggregated to match the CGE model’s sectoral level of aggregation. In addition, the paper carries on a validation exercise to compare the model’s predictions with real trade data developments. The modelling simulation results show that the impact of the ban on total exports of the EU, the USA, Norway, Canada and Australia are limited. Total extra-EU exports decline by merely 0.12%. Nevertheless at a disaggregate level there are sectors – ‘vegetables and fruits’, ‘other meat’ and ‘dairy products’ – which experience two digit percentage change declines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".