The impact of output price support on smallholder farmers' income: evidence from maize farmers in Ghana
Bibliographic record
Abstract
Instability in smallholder farmers' income in developing countries due to unstable farm prices has been a challenge for farmers and agricultural policymakers over the years. Sustained price stabilization mechanisms are mostly lacking. In some countries, output price support has been initiated to stabilize incomes and as an incentive to enhance farmer investment and boost production. This paper investigates the impacts of output price support on smallholder farmers' income in Ghana, using a household and farm-level data from 252 beneficiaries and 268 non-beneficiaries of buffer stock operations in Ghana. We applied the Coarsened Exact Matching and Propensity Score Matching methods to balance the data among the two groups. We estimate the smallholder farmer income effect from participating in buffer stock operations by combining the matching methods in a regression framework. The results affirm that buffer stock operations increase the incomes of participating smallholder farming households by at least 12%, providing evidence that output price support via buffer stocks is a critical tool for improving incomes and alleviating poverty among farmers in Ghana. The results further indicate that age, gender, access to market, and use of extension services, as well as transport and packaging costs, drive the participation of smallholder farmers in the buffer stock operations in Ghana. The findings are relevant to local policymakers and development partners who develop tailored interventions to stabilize and increase income for smallholder maize farmers in Ghana.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".