How U.S. Farm Programs and Crop Revenue Insurance Affect Returns to Farm Land

A simulation model incorporating price and yield variability is used to examine the impact of government farm program and crop revenue coverage (CRC) insurance payments on the probability distribution of returns to land. Results indicate that Marketing Loan Program payments have the greatest impact on both the mean and standard deviation of returns. Agricultural Market Transition Act payments shift the distribution of returns without changing the variability, creating a reduction in relative risk. Market loss assistance payments increase the mean, reduce variability, and increase skewness. When combined, farm programs substantially increase the value that risk-averse producers place on the residual returns to land and substantially reduce the certainty equivalent value of CRC.     

OPTIMIZATION AND DECISION MODELS: THE USE OF STATISTICAL SEARCH PROCEDURES1

This article identifies the basic elements of a decision model and the errors that can be made in the development of optimizing decision models–errors of specification and errors of exploration. Specification errors are defined as those errors that result from the use of inaccurate structural relationships and parameters, whereas exploration errors occur in the process of searching the decision space for the optimal response. A number of systematic search procedures that can be used to reduce the exploration error in simulation models are reviewed. These procedures include random search methods, experimental designs, “learning mechanisms,” and “hill climbing.” It is suggested that if the availability of these procedures is recognized, the choice of a particular empirical model can be based on its ability to accurately portray the decision problem rather than the availability of an error-free optimizing algorithm. ?article traile des principes caraclerisliques fondamentaux concernant les modèles de norme ainsi que des erreurs possibles dans la mise au point de modèles de norme à réglage optimum, surtout des erreurs dues à la spécification et à?exploration. Les erreurs dites spécif ques proviennent de ?emploi de rapports el de paramèlres fonctionellement fauiifs ou inexacts, tandis que les erreurs ?exploration sont le résullat de calculs imprecis de ?espace denorme convenable pour obtenir une réponse optima. ?auteur présente une serie ?exemples en vue de systématiser les procédes valables qui aboulissenl a une réduction des erreurs ?exploration à?aide de modèles-simulaieurs. Parmi les procédes convenables on propose une méthode de déterminations au hasard, certains projets expérimentaux, des “mecanismes ?assimilalion intellectuelle” et des “expériences de tâtonnemenl progressif” L'auteur en conclut que si ?on reconnat et qu'on emploie les méthodes proposées, il est bien possible de mettre au pooint un modèle expérimental ou empirique capable de servir de modèle problemalique bien exact plutol que de s'appuyer foncieremenl sur ?emploi ?un algorithme optimum valable.