Analysing sub-Saharan Africa trade patterns in the presence of regional trade agreements - the case of COMESA, SADC, ECCAS and ECOWAS
Bibliographic record
Abstract
This paper uses data on the four largest Regional Trade Agreements (RTAs) insub-Saharan Africa to argue that the dynamic form of the gravity equation is the appropriate model to estimate the effect of RTAs on intra-African trade. The paper also suggests a better approach to examining trade relationship between members of RTAs and nonmembers. The paper uses System Generalized Method of Moments estimator to overcome econometric issues associated with estimating dynamic models with persistent variables. The paper reports three important findings. First, a formal model selection test confirmed that the dynamic gravity model performs better than the static version. Second, the creation of COMESA and SADC has led to significant increase in trade among members. ECOWAS has increased intra-ECOWAS trade but in total has reduced intra-African trade. ECCAS has had a negative impact on both intra-ECCAS and extra-ECCAS bilateral trade flows. Third, our proposed approach to examining member-nonmember trade relationships provided the true estimates as compared to results from employing the usual approach in the literature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".