Implications of the Fertilizer-Subsidy Programme on Income Growth, Productivity, and Employment in Ghana
Bibliographic record
Abstract
We examined the economy-wide impact of a fertilizer-subsidy programme in Ghana with a focus on agricultural-sector productivity, overall economic growth, employment, and welfare. We adopted a modified version of the standard PEP-1-t model. Our results suggest that the fertilizer-subsidy programme improved GDP growth and sector-based productivity— notably, in the main agricultural subsectors and the food industry. Specifically, compared to the business-as-usual scenario, the implementation of the fertilizer-subsidy programme in 2017 improved the productivity of the maize, sorghum, and rice subsectors by about 8.3%, 4.5%, and 3.8%, respectively. These effects were, however, about four-times, three-times, and six-times higher in 2020 than their 2017 levels, respectively. We also observed important positive effects on the value-added of the food industry, indicating the presence of a backward linkage with agriculture. The unemployment rate among skilled labour (except urban skilled labour in agricultural) fell under the programme, and the decline in unemployment was relatively more pronounced for rural skilled labour in non-agricultural activities. In addition, we found evidence of positive effects on household consumption and, subsequently, on welfare. Based on these findings, we recommend that the fertilizer-subsidy programme be implemented and, if possible, extended beyond its planned implementation period.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".