A considerable number of farmer-owned ethanol plants have been built in the past few years, with many more planned. In part, farmers' investment in ethanol plants is an attempt to increase grain prices in their local market. We examined this issue by estimating the impact on local grain prices of twelve ethanol plants that opened from 2001 to 2002. We find that these new ethanol plants increased local grain prices, but the impact was not uniform around the plant. Markets downstream from a new plant, where prices tend to be higher, experienced a smaller price impact from the ethanol plant. On average across plants, corn prices increased by 12.5 cents per bushel at the plant site, and some positive price response was felt 68 miles away from the plant.

A point-space model of interregional trade is used to define market integration and to explore its implications for modeling spatial price relationships. This analysis indicates that spatial prices are related nonlinearly, contrary to much of the work on spatial price analysis which uses linear models. As an empirical example, corn market integration along the Mississippi River is examined during the Midwest flood of 1993. Higher transport costs during this period significantly reduced the extent of integration and thereby decreased excess demand shock transference across regions.

The current price of a futures contract conveys the expected price, as determined by the market, at the contract expiration date. However, futures prices do not indicate the degree of certainty that the market places on this price forecast. To obtain the market's certainty level about the price expectation, one must look at option prices. In fact, through option prices one can uncover the ex ante price probability distribution function.One factor which influences the shape or moments of the price distribution is information. New information induces traders to reevaluate their current expectations about future economic conditions. If traders' expectations change, then one should observe changes in the moments of the distribution. For example, information that makesThe authors are grateful to Scott Irwin, Paul Fackler, and Randy Fortenbery for their insightful suggestions and discourse on this subject.

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