Agricultural Growth and Poverty Reduction InThe D.R.Congo: A General Equilibrium Approach
Bibliographic record
Abstract
This paper evaluates the contribution of agricultural growth to poverty reduction in the D.R.Congo over the projection period 2013 - 2020. It raises questions over the investment options to sustain such growth effort. We use a recursive dynamic computable general equilibrium model combine with survey-based micro simulation analysis at both national and sub national levels. We assume in the simulations that the additional growth in total factor productivity is an exogenous factor and find the following results. First, we find that 8.21 % agricultural annual growth rate is more effective at reducing poverty and achieves the goal of halving poverty by 2020. Second, we identify agricultural investment priorities and the required levels of public spending to achieve such growth and poverty reduction goals. We further analyze the growth at the subsector level and find that cereals and roots are more pro-poor. From this perspective, agricultural strategy based on expanding food crops production should be afforded the highest priority.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".