Groundnut Sector Liberalization in Senegal: A Multi-household CGE Analysis
Bibliographic record
Abstract
In Senegal, the poverty reduction strategy is taking place in a context where international trade liberalization impacts the agricultural sector as a whole, and the groundnut sector in particular. Against this backdrop, we have developed a micro-simulated multiple-household computable general equilibrium model similar to the one proposed by Decaluwé et al. (1999b, How to Measure Poverty and Inequfality in General Equilibrium Framework, CREFA Working Paper No. 9920, Université Laval, Québec). Five simulations have been carried out in order to assess their impact on several levels—namely the macroeconomic, sector-based and household levels. The first two simulations concern tariff reforms, whereas the last three examine the external shocks resulting from a change in export prices on the world market (namely, for groundnuts and groundnut oil). The point of these simulations is to assess how the liberalization of the groundnut industry and the privatization of the Société Nationale de Commercialisation des Oléagineux du Sénégal—two major elements in the Framework Agreement—may impact households, and thus to see in what ways these economic reforms relate to poverty and income distribution. The results show that reducing the special tax on edible oils is positive in terms of poverty effects and the reduction of world prices of groundnut has relatively strong negative effects on poor households if farmers are not protected via a fixed price.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".