Bibliographic record
Abstract
Abstract Globally, 81 countries are now part of a customs union (CU), following the rapid proliferation of this type of trade agreement in past decades. Much of this growth has been driven by countries “upgrading” their links from a free trade agreement (FTA) to CU. At the same time, the rapid formation of new FTAs among countries that had no prior agreement in place has largely overshadowed this growth, making CUs the silent success of regional integration. Using the canonical regionalism model, augmented to allow for political bias towards firm interests, we investigate the endogenous choice of trade agreement. We show it is generally politically viable to move from FTA to CU, because such a move is rent‐creating; but for countries without a trade agreement in place, it may be optimal to form an FTA as a stepping stone to reduce the risk of political derailment. Importantly, forming a CU is consistent with member social welfare maximization: as long as trade with the rest of the world does not cease entirely, a CU leads to higher social welfare than either FTA or no agreement. These gains come at the expense of third‐country welfare. If past trends continue, one can expect more FTAs to be upgraded to CU with associated adverse consequences for outsiders.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".