Determinants of Fish Trade Flows in Africa
Bibliographic record
Abstract
Given that fish and fishery products are ranked among the most traded food commodities globally, with developing countries accounting for the bulk of the world’s fish exports, the analysis of fish trade flows is of key importance for any policy measure in the fisheries sector. This study evaluates the determinants of fish trade flows by applying the generalized gravity model. Using panel data covering a period of 14 years for 54 African countries, the gravity model is estimated using the Tobit regression to overcome estimation challenges in the presence of zero trade observations. The results suggest that a 1% increase in exporters’ GDP, importers’ GDP, population, exporters’ fish production, and countries sharing a common border increased fish trade flows by 8%, 14 %, 4%, 36% and 60%, respectively. On the other hand, importers’ fish production, and distance reduced fish trade flows by 5% and 17%, respectively. The results further shows that the belonging to ECOWAS, EAC, SADC and AMU has significantly enhanced intra-fish trade flows thereby contributing to gross trade creation for fish. The results indicate that the current demand for fish is very high such that current production is unable to meet the consumption needs. This calls for consolidated efforts in investment and development of the aquaculture sector as an alternative to the dwindling fish supplies from the wild environment. The findings also demonstrate the need for regional blocs to improve the transport networks on the continent by, among others, adopting a regional cooperation strategy centered on infrastructure development.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".