Pre‐trends and trade effects of temporary trade barriers
Bibliographic record
Abstract
Abstract The identification of the trade effects of temporary trade barriers (TTBs) hinges critically on the parallel pre‐trends assumption and the counterfactual continuation of pre‐trends. Using US TTB investigations between 1993 and 2015, we document large and gradual pre‐trends: relative to imports of the same sector and country, import quantities of targeted goods increase by 50% and prices drop by 16% over the six years before the investigation. Trade effects are significantly smaller under a static approach than under an event study design. Under the latter, the long‐run effects differ significantly depending on the assumed continuation of pre‐tends. Under the parallel pre‐trends assumption, the trade destruction after 5 years is 43%; however, if the 6‐year linear pre‐trend is extrapolated, it is 64%. The treatment of pre‐trends is critical to determining the net per good trade effects. By examining pre‐trend heterogeneity, we also provide new insights into the “de facto” application of TTBs.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".