Groundnut Sector Liberalization in Senegal: A Multi-household CGE Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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