The role of non‐discrimination in a world of discriminatory preferential trade agreements
Bibliographic record
Abstract
Abstract In a three‐country model of endogenous trade agreements, we study the implications of the most‐favoured‐nation (MFN) clause when countries are free to form discriminatory preferential trade agreements (PTAs). Under current rules of the World Trade Organization (WTO), although non‐member countries face discrimination at the hands of PTA members, they themselves are obligated to abide by MFN and treat PTA members in a non‐discriminatory fashion. The non‐discrimination constraint of MFN reduces the potency of a country's optimal tariffs and therefore its incentive for unilaterally opting out of trade liberalization. Thus, MFN can act as a catalyst for trade liberalization. However, when PTAs take the form of customs unions, the efficiency case for MFN as well as its pro‐liberalization effect is weaker because one country finds itself deliberately excluded by the other two as opposed to staying out voluntarily.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".