Groundnut Export Tax in Senegal: Winners and Losers
Bibliographic record
Abstract
Groundnuts are the most common cash crop and the main source of income for farmers in Senegal. Previously marginal, groundnut exports surged between 2011 and 2013. This new dynamic motivated the Government of Senegal to introduce a tax on groundnut exports in 2017. Senegal is a price-taker in the international groundnut market. Thus, the ex-ante simulation of the export tax on groundnuts results in a decreasing surplus for groundnut producers, while the surpluses of groundnut processors, the Government, and consumers increase. However, the positive effect on consumers is reversed if the introduction of the export tax is associated with a public investment-led groundnut productivity increase. The tax appears to be biased in favor of the export-oriented groundnut oil industry. Although the groundnut productivity increase mitigates the producers’ loss, it widens the benefit accruing to the groundnut processors. The induced increase of groundnut oil exports and the exchange rate effect exacerbate the producers’ loss. The associated negative income effect exceeds the positive price effect, leading to a decline in consumers’ surplus. Therefore, the introduction of an export tax does not necessarily increase consumers’ surplus in a country with weak market power. The economic structure and the external trade features of the country are as relevant as the fiscal policy decisions associated with the implementation of the trade reform.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".