Crop minimum support price versus cost subsidy: Farmer and consumer welfare
Bibliographic record
Abstract
Besides using earmark budget to support farmer cost subsidies, governments in many developing countries use minimum support price (MSP) as an alternative subsidy scheme to (i) safeguard farmers' incomes against vagaries in crop price and (ii) ensure sufficient crop production. Among different MSP schemes, we focus on the credit‐based MSP scheme under which a government will not take any possession of a crop; instead, it will credit farmers if the prevailing market price is below the prespecified MSP. In this paper, we consider a market consisting of infinitesimally small, rational, and strategic farmers (with heterogeneous production costs) who face market and yield uncertainties. Our equilibrium analysis reveals that (i) although both cost subsidy and MSP induce more production, cost subsidy leads to a higher crop production than MSP; (ii) MSP improves farmer's and consumer's surpluses; however, cost subsidy improves consumer's surplus but it can decrease farmer's surplus, which is unexpected; (iii) although both programs achieve the same optimal net value (i.e., sum of farmer's and consumer's surpluses minus shortage cost and expenditure), MSP always offers higher farmer's surplus than cost subsidy; and (iv) it is beneficial to invest only in cost subsidy, in both cost subsidy and MSP, and only in MSP, when the budget availability is low, moderate, and high, respectively, so that the net surplus (i.e., sum of farmer's and consumer's surpluses less the shortage cost) is also maximized along with the net value generated being maximized.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".