EFFECT OF HUMAN CAPITAL ON MAIZE PRODUCTIVITY IN GHANA: A QUANTILE REGRESSION APPROACH
Bibliographic record
Abstract
Agriculture continues to play an important role in the economy of most African countries. Thus, productivity growth in agriculture is necessary for economic growth and poverty reduction of the region. While, theoretically, investing in human capital improves productivity, the empirical evidence is somewhat mixed, especially in developing countries. In Ghana, maize is associated with household food security, and low-income households are considered food insecure if they have no maize in stock. But, due to low productivity, Ghanaian farmers are yet to produce enough to meet local demand. Using quantile and OLS regression techniques, this study contributes to the literature on human capital and productivity by assessing the effect of human capital (captured by education, farming experience and access to extension services) on maize productivity in Ghana. The results suggest that although human capital has no significant effect on maize yields, its effect on productivity varies across quantiles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".