The Efficacy of Regional Trade Agreements, 1958–2006: The Effect of Institution Creation on Market Expansion
Bibliographic record
Abstract
This article examines the efficacy of regional trade agreements (RTAs) in promoting bilateral trade through a sociological lens that stresses institutional underpinnings of the market. Most RTAs are driven by economic neoliberalism and mainly focus on “negative integration” (direct removal of trade barriers such as tariffs and other regulations). Not all RTAs incorporate elements of “positive integration” (deliberate establishment of institutions supporting new markets). Using a large data set on international bilateral trade from 1958 through 2006, this study finds that incorporation of positive integration in an RTA increases its actual efficacy in promoting bilateral trade. It further distinguishes two major types of positive integration: (1) establishment of market institutions that directly regulate cross‐border markets and (2) establishment of social institutions that deal with social consequences of market expansion. Both types promote bilateral trade. The higher the level of positive integration, the more effective the RTA is in promoting bilateral trade. Overall, this study lends support to the sociological insight that creation of necessary institutions is essential for market expansion across borders. It also implies that positive integration has the potential to reconcile the seemingly contradictory goals of trade expansion and social protection.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".