The Impact of Agricultural Extension on Farm Production in Resettlement Areas of Zimbabwe
Bibliographic record
Abstract
This study contributes to the literature on the impact of farmer contact with agricultural extension services on farm productivity, drawing on a unique panel sample of households residing in three resettlement areas of rural Zimbabwe. It pays particular attention to the potential confounding effects of the biases identified by Birkhaeuser, Evenson, and Feder. Specifically, we exploit the longitudinal nature of our data to estimate the impact of extension on the value of crop production per hectare, with and without controls for unobservable household fixed effects. The attraction of this estimator is that the differencing process rids the specification of the correlation between extension and the disturbance term. We find that after controlling for innate productivity characteristics and farmers' ability using household fixed- effects estimation, access to agricultural extension services, defined as receiving one or two visits per agricultural year, raises the value of crop production by about 15%. This parameter estimate is statistically significant. Another unique feature of these data are, for a subsample, extension worker assessments of farmers' ability. We find that farmers with above-average ability are indeed more productive, producing 40%-50% higher output per hectare of cropped area. Controlling for innate productivity using locality dummies, farm plot characteristics, and farmers' ability using these assessments of ability, we continue to obtain a positive association between access to extension and productivity, an association that is equal in magnitude to our fixed-effects results. However, we also find considerable variability in these parameter estimates across individual crop years.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".