Does Public Spending Trigger Agricultural Productivity Growth in Africa?
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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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".