Nonfarm employment, agricultural intensification, and productivity change: empirical findings from Uganda
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article uses panel data from the Living Standards Measurement Study‐Integrated Surveys on Agriculture for Uganda to assess the farm‐level effects of nonfarm employment on agricultural intensification and productivity change. A sample selection model is used to account for both unobserved heterogeneity and potential simultaneity between agricultural production and nonfarm income. Results show that nonfarm employment can have differential impacts on farm technology intensity and productivity. Nonfarm income is found to have a positive impact on farm hired labor and improved seed intensity; a negative effect on on‐farm family labor use; and no significant impact on fertilizer, soil water management, and joint use of farm technologies. The econometric evidence also indicates that agricultural productivity declines as nonfarm income increases. Taken together, our findings reveal important trade‐offs between nonfarm employment and income and farm productivity growth under smallholder agriculture. The results indicated that targeted policies are required to reduce these potential trade‐offs between nonfarm employment and agricultural intensification and productivity change.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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