Unlocking the impact of US free trade agreements on industries with a synthetic control approach
Bibliographic record
Abstract
Abstract This study explores the industry‐level effects of the FTAs that the United States signed with Chile, Australia and the Dominican Republic between 2004 and 2007. Employing a synthetic control approach, we uncover heterogeneity in post‐FTA export growth across countries and industries. The study reveals that only a limited subset of industries in the US, which contributed to roughly one seventh of pre‐FTA exports, experienced post‐FTA export gains. No single industry consistently benefited from the FTAs with all three partners. This heterogeneity is present in countries where FTA‐induced aggregate export growth is absent, as well as in those where only a few industries drive the aggregate export growth. Export increases were also concentrated in a limited range of products. Notably, only exports to Chile led to increased export intensity and diversification at both the aggregate and industry levels. These findings, robust to various specifications and estimation methods, highlight the substantial variation in FTA effects across industries and partner countries.
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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 | 0.006 | 0.013 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".