Market Structure and the Value of Agricultural Contingent Claims
Bibliographic record
Abstract
In this paper, we evaluate the proposition that market structure, including supply and demand elasticities, plays a significant role in influencing equilibrium price dynamics. We show that the more inelastic the demand and/or supply of a commodity is, the more price risk will be faced by producers of that commodity. In general one can expect greater volatility from commodities with both demand and supply being inelastic, and the least when they are both elastic. We illustrate how the elasticities of supply and demand can impact the prices of contingent claims on agricultural commodities including revenue insurance. Dans le présent article, nous évaluons la proposition selon laquelle la structure de marché, y compris l'élasticité de l'offre et de la demande, influence la dynamique du prix d'équilibre. Nous montrons que plus la demande et/ou l'offre d'un produit de base est inélastique, plus les producteurs de ce produit de base sont confrontés au risque de prix. En général, on peut s'attendre à une forte volatilité des produits de base pour lesquels l'offre et la demande sont à la fois inélastiques, et à une faible volatilité lorsqu'elles sont élastiques. Nous faisons ressortir de quelle façon l'inélasticité de l'offre et de la demande peut influencer les réclamations éventuelles concernant des produits agricoles de base, y compris l'assurance−revenu.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".