Joint market participation choices of smallholder farmers and households’ welfare: evidence from Senegal
Bibliographic record
Abstract
Purpose There is much evidence in the literature showing the benefits of input market participation on farmers’ welfare. The same is true for participation in marketing. However, there are very few studies on the expected benefit of input market participation and marketing. This study fills this gap by examining the issue in the Senegalese context for food and cash crops. Design/methodology/approach The authors estimate a multinomial endogenous switching regression using a highly detailed 2017 agricultural survey in Senegal. They first identify factors that shape farmers’ decision to participate in the input market and marketing and then assess the impact of market participation choices on farmers’ profits. Findings The results show that the most profitable market participation regime depends on the crop under consideration. For food crops, joint participation in markets maximizes profit per hectare, while for groundnuts, the main cash crop in Senegal, participation in the input market is not necessary to maximize farm profit. Research limitations/implications Using panel data would improve the quality of estimations (time-variant effects) and help to consider the role of risk in output and input markets. Originality/value This paper helps to characterize different profiles of farmers based on their market participation and crop choices and provide policymakers with recommendations for maximizing farmers’ profit.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".