A quadratic programming model of the Appalachian steam coal market
Abstract The authors report on an extension of recent modelling efforts to analyse the Appalachian steam coal market using a programming approach. The background to these modelling efforts is examined first. The particular aspects of the steam coal market to be modelled are then presented and the corresponding coal model described. Empirical results are analysed for the 1973 base solution and then compared to alternative solutions embodying variations in the parametric structure. Conclusions are drawn regarding potential policy applications.
A Theory of Spatially Separated Markets
THE THEORY OF SPATIALLY SEPARATED MARKET for one commodity was first investigated by Enke [2] and Samuelson [9] in the early fifties. Enke solved a three region model by electric analogue and Samuelson, after converting the model into a maximizing problem, conjectured some comparative statics results on the basis that each regional excess supply curve was positively sloped. This condition, it will be shown, is the condition for Hicksian perfect stability in the model. Samuelson did not investigate the conditions for dynamic stability and it turns out that very little comparative statics information follows from the knowledge that the spatial system is dynamically stable. In addition, certain comparative statics relations stated by Samuelson -are incorrect. The purpose of this paper is to develop this theory rigorously, to derive new results concerning the one-commodity case and to extend the analysis to the rn-commodity case. The one-commodity, n-region spatial-equilibrium model can be formulated as follows: For each of n regions there exists an excess demand curve EDi and an excess supply curve ESi in terms of the local price in each region:
Impact of Farm Size on the Bidding Potential for Agricultural Land
A theoretical model is constructed to determine the maximum bid price that would be made for an acre of land by a decisionmaker with a given set of characteristics, capabilities, and expectations. The variables included that have an impact on the maximum bid price are net income, income variability, wealth, degree of risk aversion, marginal income tax rate, rate of pure time preference, and expected rate of growth in land income and prices. A numerical illustration of the model, developed for cash-grain farmers in Iowa, demonstrates the relative importance of the variables in determining bid-price differentials among farm-size classes.