How States Ration Flexibility: Tariffs, Remedies, and Exchange Rates as Policy Substitutes
Bibliographic record
Abstract
A close look at the commitments of World Trade Organization (WTO) members presents a striking paradox. Most states could raise their duties significantly before falling afoul of their WTO obligations. Moreover, such “binding overhang” varies between countries: some could more than double the amount of trade protection they offer overnight, whereas others are tightly constrained. What accounts for this variation? The author argues that more flexibility is not always better: obtaining it and subsequently using it are both costly. Rather than maximize flexibility, states thus seek an optimal amount. If they have access to policy space through other means, such as currency devaluations and trade remedies, they will exercise restraint in seeking binding overhang. The same supply-side logic holds at the domestic level: governments strategically withhold binding overhang from industries that are able to rely on trade remedies, despite the fact that these tend to have the greatest political clout.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".