Does Agriculture Matter for Economic Development? Empirical Evidence from Nigeria
Bibliographic record
Abstract
Abstract: In this study we aimed at answering the question, ‘Does agriculture matter for economic development in Nigeria? ’ Life expectancy is modeled against agricultural output and agricultural expenditure, amongst other variables. Agricultural output is also modeled against a host of socio-economic, natural and human factors, which influence agricultural productivity. Applying Augmented Dickey-Fuller unit root test, Ordinary Least Squares, and the Newey-West method on secondary data and dummy variable used in the study, it was found that agricultural output has negative and significant impact on life expectancy in Nigeria. The impact of agricultural expenditure was found to be positive but nonsignificant. Real gross domestic product and industrial output were also found to influence life expectancy. Careful examination of the hypothesized socio-economic factors (political instability and industrial output), natural factor (rainfall), and human factor (carbon emission) showed that only industrial output and rainfall matter for agricultural output in the country: both variables have positive impacts on agricultural output. The study submits that as much as agriculture may matter for economic development, reliance on the sector alone without corresponding and simultaneous development of other crucial sectors such as education, health, and industry will not yield positive fruits for economic development in 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".