We use data from the Portland Fish Exchange of Portland, Maine, to illustrate how to estimate a demand function using regression analysis. At the Exchange, fishing boats unload thousands of pounds of...


We use data from the Portland Fish Exchange of Portland, Maine, to illustrate how to estimate a demand function using regression analysis. At the Exchange, fishing boats unload thousands of pounds of cod and many other types of fish on a daily basis. The fish are weighed and put on display in the Exchange’s 22,000-square-foot refrigerated warehouse. Buyers inspect the quality of the day’s catch. At noon, an auction takes place in a room overlooking the fish in the warehouse. Fleece-clad buyers representing fish dealers and restaurants sit facing a screen, an auctioneer, and representatives of the sellers. A screen at the front of the room shows bid information. Thousands of pounds of fish sell rapidly. The quantity of fish delivered each day, Q, varies due to fluctuations in weather and other factors. Because fish spoils rapidly, all the fish must sell immediately. Therefore, the daily auction price for cod adjusts to induce buyers to demand the amount of cod available on that particular day. The price-quantity combination for cod on any particular day represents a point on the cod demand curve. For each day it operates, the Portland Fish Exchange reports the quantity (in pounds) of each particular type of fish sold and the price for that type of fish. For example, on June 8, 2015, 2,946 lb of large white hake sold for $0.97 per lb. A collection of such observations provides a data set that can be used for demand analysis using regression. Here, quantity is the explanatory variable. Whatever quantity comes to market sells that day. Price adjusts to ensure that the amount of fish available meets the buyers’ demand. As the quantity brought to market “explains” the price, we estimate an inverse demand curve.

Jan 01, 2022
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