Nigeria’s cocoa exports: a gravity model approach
Bibliographic record
Abstract
ABSTRACT: What are the major factors affecting Nigeria’s cocoa export flows? In answering this question, the authors suggest a commodity-specific gravity model with three different analytical approaches, (the Heckman Sample Selection Model, the Generalised Least Square, and the Poisson Pseudo Maximum Likelihood), based on a period of 24 years of panel data for Nigeria and it’s 36 importing partners to estimate the models. The results showed that GDP, exchange rate policy, WTO, EU, and colonial link are positively associated with the Nigerian cocoa export flows. Further, the negative impact of the GDP per capita, landlocked, distance, AU, and ECOWAS are observed. The need for the expansion of exports to the trading partners, especially the EU members (Netherlands, Germany, France, United Kingdom, Belgium, Spain, etc.), Canada, Malaysia, and the USA is particularly highlighted. These results are important for the formulation of future trade policy that could boost up the Nigerian cocoa exports. This would eventually contribute to the diversification of the Nigerian exports and also enhance the country’s foreign earnings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".