Trade Policy, Infrastructure and Agricultural Output in Nigeria
Bibliographic record
Abstract
The collapse of the international price of crude oil in 2015 and its attendant negative consequences on government fiscal capacity and development efforts re-echoed the need for Nigerians to return to agriculture as the surest means of conserving foreign exchange and revamping productive capacity. Within this context, this paper deploys the autoregressive distributed lag (ARDL) econometric methodology to investigate the impact of Nigeria’s trade policy and infrastructural development on agricultural value added. Findings show that in the long run Nigeria’s trade liberalization policy is a disincentive to the growth of the agricultural sector value added, while key components of infrastructure (roads, telecommunications, and electricity consumption) had a significant relationship with the agricultural sector. We advocate guided trade liberalization wherein, while embracing the principles of conventional trade deregulation, the government properly articulates the weakness of the economy’s productive structure and encourage farmers and local producers to attain maturity. Specifically, the current ban on some selected food items should be consolidated, without which Nigeria would continue to be a net food importer. Goveronment might consider studying and implementing the African Development Bank’s Infrastructure Action Plan for Nigeria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".