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There are nine Iowa counties of interest in this project. These include the four counties surrounding the town of Grinnell (Marshall, Tama, Jasper, and Poweshiek), and the five in the vicinity of Fairfield (Wapello, Jefferson, Davis, Van Buren, and Henry).
A map of the state of Iowa with the two regions outlined is shown in the attached file entitled "Outline of Two Regions."
Grinnell Region
Total Farmers' Markets: 7
Total Number of Residents: 113,442
Number of Farmers' Markets per 100,000 Residents: 6.17
Marshall County:
Tama County:
Jasper County:
Poweshiek County:
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Fairfield Region
Total Farmers' Markets: 6
Total Number of Residents: 88,918
Number of Farmers' Markets per 100,000 Residents: 5.62
Wapello County:
Jefferson County:
Davis County:
Van Buren County:
Henry County:
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Iowa Farmers' Market directory information from http://www.agriculture.state.ia.us/farmermarket.asp
Iowa county maps from http://www.iowa.org
Iowa county population and income data from http://www.census.gov
Iowa county area data from http://county-map.digital-topo-maps.com/iowa.shtml
Check out http://www.foodroutes.org/
Find out where your food comes from, how you can buy local, why you should buy local, Community Supported Agriculture (CSA), "Buy Fresh, Buy Local," and much more.
Located here 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
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