ENERGY-RELATED INPUT DEMAND BY CROP PRODUCERS

Demand and Price Analysis, Resource /Energy Economics and Policy,

TARGET MOTAD FOR RISK LOVERS

Although risk analyses of discrete alternatives often identify at least one efficient set for persons who prefer risk, preference for risk is usually ignored when the decision variables are continuous. This paper presents a version of Target MOTAD which can be used when there is preference for risk.risk, target MOTAD, risk seeking, risk lovers, Risk and Uncertainty, D81, Q12,

VOLATILITY OF CASH CORN PRICES BY DAY-OF-THE-WEEK

The volatility of St. Louis cash corn bids by day-of-the-week is examined for the period September 1992 through August 1999. Thursday to Friday, Friday to Monday and Friday to Tuesday (with a holiday on Monday) price changes tend to be larger than other day-to-day changes.Financial Economics, Marketing,

IDENTIFYING THE SET OF SSD-EFFICIENT MIXTURES OF RISKY ALTERNATIVES

Target MOTAD and other direct utility-maximization models provide one way of computing SSD-efficient mixtures. These models are appropriate when the utility function is known and can also be used to identify part of the set of SSD-efficient mixtures even when the utility function is not known. However, they do not always identify all SSD-efficient mixtures. A grid method was proposed by Bawa, Lindenberg, and Rafsky. A third approach, which extends the work of Dybvig and Ross, is presented here. It is illustrated by applying it to data from Anderson, Dillon, and Hardaker.Risk and Uncertainty,

SHADOW PRICE IMPLICATIONS OF SEVERAL STOCHASTIC DOMINANCE CRITERIA

Stochastic dominance criteria can be, but seldom are explicitly, applied to problems having continuous variables. A previously developed model is modified to facilitate exploration of sets of shadow price vectors for decreasing (non-increasing) absolute risk aversion stochastic dominance (DSD), a combination, TGSD, of third degree stochastic dominance (TSD) and generalized stochastic dominance (GSD) and a combination, DGSD, of DSD and GSD. The model is illustrated by applying it to two risk efficient (primal) solutions of a problem by Anderson, Dillon and Hardaker. For each of the two primal solutions and, where relevant, three risk aversion coefficient intervals, selected aspects of the sets of shadow price vectors consistent with TSD, DSD, TGSD and DGSD are compared with each other and with sets of shadow price vectors consistent with GSD and second degree stochastic dominance (SSD).Demand and Price Analysis,

IMPACTS OF ENERGY ALLOCATION ON INCOME AND RISK FOR CROP PRODUCERS

A quadratic programming model was used to explore the impacts of fuel allocation levels on crop producers. Impact on. expected income levels, risk levels and Btu consumption are evaluated. An allocation level which reduced diesel fuel consumption by about 20 percent would reduce expected Income by about $77 per gallon, reduce the standard deviation of income by about$21 per gallon, and reduce total energy consumption by about 220 thousand Btu's per gallon. The use of energy based inputs other than diesel fuel would not change much until more drastic reductions in diesel fuel allocations were imposed

The effect of energy and crop price changes on output response of risk averse farmers - a quadratic programming approach

A quadratic programming model was used to explore producers' output response to changes in energy and crop prices. Risk considerations were also incorporated. Solutions were obtained for several price and risk aversion levels. Approximate crop response functions were estimated. Output elasticities were computed for selected price combinations

Necessary conditions for first, second and third degree stochastic dominance efficiency of enterprise mixtures

Variations of a result by Dybvig and Ross provide necessary conditions for the FSD, SSD and TSO efficiency of enterprise mixtures. Several of their perfect market portfolio problem results are adapted to the enterprise mixture problem. The results imply that the sets of enterprise mixtures which satisfy the necessary conditions for FSD, SSD and TSO efficiency are unions of finite numbers of convex subsets. Linear programming can be used to determine whether a particular mixture and the (appropriately defined) convex subset to which it belongs satisfy these conditions. These ideas are illustrated by applying them to a simple example