Developing‐Country Benefits from MFN Relative to Regional/Bilateral Trade Arrangements
Bibliographic record
Abstract
Abstract Using a general‐equilibrium model of world trade, this paper evaluates the benefits of most‐favored‐nation (MFN) treatment to developing countries in multilateral relative to bilateral or regional trade agreements, from three sources. First, developing countries may be able to free‐ride on bilateral tariff concessions exchanged between larger countries in MFN‐based GATT/WTO rounds. Second, MFN benefits developing countries by restricting discriminatory retaliatory actions by other countries, evaluated here by a non‐ cooperative Nash tariff game. Finally, MFN changes threat points in bargaining and hence affects the bargaining solution of multilateral MFN‐based trade negotiation compared to a bilateral/regional arrangement. The authors find that the benefits to developing countries are small in the first case as the tariff rates are already low, and the benefits are small in the second case as the optimal tariffs under unconstrained retaliation are not very asymmetric. Benefits from the third case are large as large countries can extract large side‐payments if they bargain bilaterally.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".