Is late really better than never? The farmer welfare effects of pineapple adoption in Ghana
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Export agriculture offers potentially high returns to smallholder farmers in developing countries, but also carries substantial market risk. In this article we examine the intertemporal welfare impact of the timing of a farmer's entry into the export pineapple market in southern Ghana. We examine whether farmers who never cultivated pineapple are better or worse off than farmers who decided to adopt pineapple earlier or later relative to their peers and experienced a significant adverse market shock several years prior to our endline survey. We use a two‐stage least squares model to estimate the causal effect of duration of pineapple farming on farmer welfare. Consistent with economic theory, we find that earlier adoption of the new crop brings greater welfare gains than does later uptake. But we find that the gains to later uptake of pineapple—just before the market shock—are small in magnitude, just 0.1 standard deviations of a comprehensive asset index, indicating that the gains to adoption may be precarious and depend on the context, in particular on the severity of prospective market shocks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it