International Economic Sanctions and Third-Country Effects
Bibliographic record
Abstract
This paper studies international trade and macroeconomic dynamics triggered by economic sanctions, and the associated welfare losses, in a calibrated, three-country model of the world economy. We assume that there are two production sectors in each country, and the sanctioned country has a comparative advantage in production of a commodity (for convenience, gas) needed to produce final, differentiated consumption goods. We consider three types of sanctions: sanctions on trade in final goods, financial sanctions, and gas trade sanctions. We calibrate the model to an aggregate of countries currently imposing sanctions on Russia (the European Union, the United Kingdom, and the United States), Russia, and an aggregate of third countries (China, India, and Turkey). We show that, instead of reflecting the success of sanctions, exchange rate movements reflect the type of sanctions and the direction of the resulting within-country sectoral reallocations. Our welfare analysis demonstrates that the sanctioned country’s welfare losses are significantly mitigated, and the sanctioning country’s losses are amplified, if the third country does not join the sanctions, but the third country benefits from not joining. These findings highlight the necessity, but also the challenge, of coordinating sanctions internationally.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".