Does Public Spending Trigger Agricultural Productivity Growth in Africa?
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Enhancing agricultural productivity growth is a key step to improving competitiveness and eradicating poverty in rural areas in developing countries. While the Comprehensive African Agricultural Development Program (CAADP) recommended increased public spending in agriculture to induce productivity growth, the extent to which expenditures affect food productivity remains an empirical question. To address this concern and provide policymakers with quantitative evidence, the authors assess the effect of two government-spending measures: agriculture budget share (BS) and research share (RS) of agricultural gross domestic product (GDP) on agriculture total factor productivity growth (TFPG) in Africa. They use a panel fixed-effect estimator to control for the country-specific characteristics in twenty-eight African economies from 1991 to 2012. They find marginal impact of approximately 6.77% of RS on TFPG after every seven years. However, the cumulative marginal impact of BS on TFPG is estimated at 7.21% over the seven years following budget allocation. These findings suggest that a BS of 14% and an RS of 15% are required for a country to double its TFPG in the following eight years. Therefore, an additional and continuous investment in research and development is required for significant productivity growth, especially in sub-Saharan Africa.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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