Africa’s missed agricultural revolution: a quantitative study of the policy options
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 Despite the widespread diffusion of productivity-enhancing agricultural technologies the world over, agriculture in Sub-Saharan Africa has typically stagnated. This paper develops a quantitative model in order to shed light on the sources of low labor productivity in African agriculture. The model provides a vehicle for understanding the mechanisms leading to low agricultural labor productivity, in particular, how the interactions between factor endowments, government investment and technology adoption may have culminated in agricultural stagnation. I calibrate the model to data for four Sub-Saharan African economies, and use this calibrated model to provide insight into policy aimed at increasing agricultural productivity in these four countries. Policies aimed at improving rural infrastructure or productivity in the non-agricultural sectors, or allowing for land transferability, would be most effective for increasing agricultural labor productivity, and would further bring increases in household welfare for each of the countries I calibrate to.
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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 | 0.001 | 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 | 0.000 |
| 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