Socially Beneficial Rationality: The Value of Strategic Farmers, Social Entrepreneurs, and For-Profit Firms in Crop Planting Decisions
Bibliographic record
Abstract
The price fluctuation in agricultural markets is an obstacle to poverty reduction for small-scale farmers in developing countries. We build a microfoundation to study how farmers with heterogeneous production costs, under price fluctuations, make crop-planting decisions over time to maximize their individual welfare. We consider both strategic farmers, who rationally anticipate the near-future price as a basis for making planting decisions, and naïve farmers, who shortsightedly react to the most recent crop price. The latter behavior may cause recurring overproduction or underproduction, which leads to price fluctuations. We find it important to cultivate a sufficient number of strategic farmers because their self-interested behavior alone, made possible by sufficient market information, can reduce price volatility and improve total social welfare. In the absence of strategic farmers, a well-designed preseason buyout contract, offered by a social entrepreneur or a for-profit firm to a fraction of contract farmers, brings benefit to farmers as well as to the firm itself. More strikingly, the contract not only equalizes the individual welfare in the long run among farmers of the same production cost, but it also reduces individual welfare disparity over time among farmers with heterogeneous costs regardless of whether they are contract farmers or not. On the other hand, a nonsocially optimal buyout contract may reflect a social entrepreneur’s over-subsidy tendency or a for-profit firm’s speculative incentive to mitigate but not eliminate the market price fluctuation, both preventing farmers from achieving the most welfare. This paper was accepted by Vishal Gaur, operations management.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".