Bibliographic record
Abstract
Abstract Although politicians and the popular press often express the desire to link retaliation in trade agreements to non‐trade issues, the WTO discourages and usually disallows cross‐retaliation even among its own agreements. In this paper, we analyze the welfare implications of cross‐retaliation. We compare two different mechanisms in a two‐country two‐sector tariff‐setting political‐economy model with incomplete information. A country may temporarily raise trade barriers in response to political pressure and the extent of this pressure is private information. In a same‐sector retaliation mechanism a safeguard action, or other limited violation of the international trade agreement, is punished by an equivalent suspension of concessions in the sector where the initial deviation takes place. In a linked, or cross‐sector, retaliation mechanism retaliatory actions may be taken in another sector or agreement. We next consider less‐than‐equivalent suspensions of concessions whereby the probability of retaliation is less than unity. We then endogenize this probability and derive its optimal level separately for same‐ and cross‐sector retaliation. We also consider the long‐run viability of these self‐enforcing trade agreements. We show that whether retaliation is certain or probabilistic a cross‐sector retaliation mechanism can generate greater welfare and self‐enforcement capability than a same‐sector mechanism unless export‐oriented political pressure in the cross‐sector targeted for retaliation is high. Although cross‐sector retaliation is usually welfare improving, there may be little additional benefit to extending retaliation to a different agreement.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".