Poverty and Inequality Impact Analysis Regarding Cotton Subsidies: A Mali-based CGE Micro-accounting Approach
Bibliographic record
Abstract
In this paper, we construct for Mali the first country-specific CGE model including a micro-simulation component so as to analyse how removing cotton subsidies in developed countries would impact poverty and inequality. To that effect, we have used the micro-accounting approach proposed by Chen and Ravallion. The issue has attracted significant attention, as it has played no small part in stalling the broader trade agenda. So far, research has been mainly carried out with a partial equilibrium analysis, whereas we use the first CGE micro-simulation model. A 17 sector CGE model comprising almost 5,000 households is used to demonstrate that removing cotton subsidies would contribute towards a significant decrease in poverty in Mali. Moreover, our results show that removing cotton subsidies while keeping other agricultural subsidies does not lessen the positive effects observed. It also appears that removing subsidies would marginally contribute towards easing inequality in Mali.
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How this classification was reachedexpand
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".