Bibliographic record
Abstract
Abstract Almost all participants in free trade agreements (FTAs) exclude at least a few products or sectors from complete tariff removal on the exports of their FTA partners. The positive tariffs that remain within an FTA are often the highest tariffs that the countries apply on an MFN basis. It seems plausible that such exclusions may be chosen because the domestic producers of these products are viewed as especially vulnerable to competition from imports from the partner country. In brief, they are especially “sensitive sectors.” We develop this idea theoretically and then test it empirically on data from 37 countries in 240 importer–exporter pairs within FTAs. We find support for the sensitive‐sector hypothesis only in the high‐income countries. We find that low‐income countries, in contrast, exempt sectors where bilateral tariff removal would be more likely trade‐diverting and therefore harmful. Our explanation for this, supported empirically, is not that they are following the advice of trade economists, but rather that they are avoiding loss of tariff revenue and may also perhaps be influenced by the greater bargaining power of richer and/or larger partners in their FTAs.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".