The price of sanctions: An empirical analysis of German export losses due to the Russian agricultural ban
Bibliographic record
Abstract
Abstract The aim of this paper is to contribute to the vivid political discussion on the consequences of the Russian agricultural import ban on the German export market by quantifying export losses that German agri‐food exporters encountered on the Russian market due to the agricultural import ban of 2014. A gravity‐type approach is used to measure the sanction effect in a panel of German agri‐food exports covering the period from January 1999 to June 2018. The ban effect is disentangled from a sequence of different geopolitically‐ and economically driven episodes. Once macroeconomic developments of the Russian economy as well as individual stages of decreasing trade cooperation in the preban period are accounted for, the import ban reduced German agri‐food exports significantly but was not the major cause. Therefore, a simple elimination of the ban will not be enough to restore trade to the presanctions level.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".