Located here [1] is an article entitled “Forecasting Consumer Price Indexes for Food: A Demand Model Approach,” by Kuo S. Huang. Huang uses an inverse demand function to assess the change in quantity demanded for a variety of goods (including beef, eggs, fruits, vegetables, cereal…) based on a one percent change in price of that good. Huang presents a chart that shows, for example, that a one percent increase in the price of poultry would result in a .84 percent decrease in the quantity demanded of poultry. He also gives figures for cross elasticity of demand: A one percent increase in the price of red meat, for example, .91 percent decrease in the quantity demanded of beef.

Huang uses six aggregate food quantities and per capita income to for forecasting consumer price indexes. Huang himself admits that relying on this information may harm the accuracy of his study.

Not only can we not be sure that his information is reliable, the figures he presents are not as useful as I first thought for our model. Most importantly, we are interested in data for very precise demographics It does us little good to see how the American in general responds to a change in price of a particular good (even if this information is accurate…): we need to know a middle income Poweshiek County resident who buys 40% of her food locally responds to a change in price. Perhaps Haung’s data and methodology will provide us with a starting point for obtaining relevant figures of our own…