Climate Variability and International Trade
Bibliographic record
Abstract
This paper quantifies the impact of hurricanes on seaborne international trade to the United States. Using geocoded hurricane data mapped to satellite tracking data for commercial ships, we identify hurricane intersections on sea-trade routes between U.S. and foreign ports. Matching the timing of hurricane-trade route intersections with monthly U.S. port-level trade data, we isolate the unanticipated effects of a hurricane hitting a trade route using two separate identification schemes: an event study and a local projection. Our estimates imply that a hurricane reduces route-specific monthly U.S. import flows by 5.4% to 16.0%, leading to an aggregate loss of 1.15% to 3.42% of annual U.S. west coast imports for an average storm season. We find no evidence of trade catching up in the months following a hurricane nor any evidence of rerouting to other ports or other transportation modes (e.g., air). Using our estimates in combination with climate scenarios from the Intergovernmental Panel on Climate Change, we quantify a range of costs of future hurricane disruptions that could occur if trade routes remain fixed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".