TRADE CREATION AND DIVERSION EFFECTS IN THE TRIPARTITE REGION: A GRAVITY APPROACH
Bibliographic record
Abstract
The paper employed the augmented gravity model to determine the trade creation and trade diversion effects of economic integration. Results indicate that the income importing country was significant at the 1% level, while the exporting one was weakly significant at the 10% level. Weighted distance was negative and significant at the 1% level. Of the country idiosyncratic factors, language was insignificant and shared border was significantly positive, while landlocked was significantly negative at 1%. The free trade area (FTA) variable indicated the degree of economic integration was significant at the 1% level. In terms of welfare effects, the study observed trade creation in SADC, but the results were inconclusive for COMESA. The EAC coefficient was significantly negative, implying that economies traded below the expected levels among themselves. The regional openness dummies indicated trade diversion effects. The EAC sign was positive and significant, implying that imports into the EAC from non-member countries in the rest of the world (RoW) were higher than the gravity model would predict, making it difficult to statistically determine the net welfare effects. The trade diversion coefficient for SADC was significantly negative. The net effect for SADC was negative, since trade diversion outweighs trade creation. The net effect for COMESA was positive, but statistically insignificant. Key Words: trade creation, trade diversion, welfare effects, economic integration, gravity, tripartite
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".