Quantifying the trade‐reducing effect of embargoes: Firm‐level evidence from Russia
Bibliographic record
Abstract
In 2014, Russia responded to sanctions imposed by a coalition of Western countries with a retaliatory import embargo. I draw on this unique case study and a customs data set on firm‐level import transactions to investigate the ramifications of Russia's counter‐sanctions on firm‐level foreign trade. Using detailed data and a triple‐difference estimation strategy, I examine micro‐level dynamics and heterogeneities that aggregate data alone do not reveal. I identify the effects of the embargo on the extensive margin (the probability that a firm imports a particular product from a given country in a particular time period) and the intensive margin (the value of a firm's import transaction) of firm‐level trade, as well as its effects on logged unit values. The main findings of this study show that the embargo had statistically significant negative impacts on extensive and intensive margins of firm‐level trade. I also pinpoint evidence of multiple exemptions from the embargo and a large degree of heterogeneity of firm‐level responses to the embargo based on firm attributes, such as firm size and government connection.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".