More Reasons Why Farmers Have So Little Interest in Futures Markets
Bibliographic record
Abstract
The use by farmers of futures contracts and other hedging instruments has been observed to be low in many situations, and this has sometimes seemed to be considered surprising or even mysterious. We propose that it is, in fact, readily understandable and consistent with rational decision making. Standard models of the decision about optimal hedging show that it is negatively related to basis risk, to quantity risk, and to transaction costs. Farmers who have less uncertainty about prices have a lower optimal level of hedging. If a farmer has optimistic price expectations relative to the futures market, the incentive to hedge can be greatly reduced. And finally, farmers who have low levels of risk aversion have little to gain from hedging in terms of risk reduction, in that the certainty equivalent payoff at their optimal hedge may be little different to the certainty equivalent under zero hedging. These reasons are additional to the argument of Simmons (2002) who showed that, if capital markets are efficient, farmers can manage their risk exposure through adjusting their leverage, obviating the need for hedging instruments.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".