Trade in times of conflict and sanctions : A gravity model analysis on the trade between the Scandinavian countries and Russia during the Russian-Ukrainian conflict
Bibliographic record
Abstract
In this thesis we study trade flows between the Scandinavian countries Denmark, Norway and\nSweden and Russia. Particularly, we investigate the trade flows in light of the Russian-\nUkrainian conflict and the following sanctions against Russia imposed by the EU. We look at\ndata on exports, imports and total trade between the Scandinavian countries and Russia and\nhow they have developed in the period from 2012 till third quarter of 2022. Insights in this\ndevelopment are interesting and important, both to evaluate the effectiveness of the sanctions\nalready imposed, and what implications these findings may have for future policy work.\nTo answer the research question, we have used both descriptive statistics and regression\nanalysis. We have used an augmented gravity model when evaluating the development of trade\nflows. To estimate the relationship between trade flows and conflict and sanction, we have\nadded dummy variables to the traditional gravity model that adhere to important events in the\nperiod.\nOur main findings are that the exports from the Scandinavian countries to Russia tend to\ndecrease from the first sanctions are imposed in 2014, mainly explained by the Russian import\nembargo on food effective from August of 2014. Further, imports from Russia have not\nsignificantly changed until second quarter of 2022, when the EU put Russia under a strict and\nwide-ranging sanction regime as a respond to the Russian invasion of Ukraine in February\n2022. Our main explanation for why it has not decreased earlier is that the goods the\nScandinavian countries imported to Russia were not directly included in the sanction\nprograms. We do not see any change in trade in periods where new sanctions are not imposed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".