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Before we go to a break, I just want to look at the relationship between these demographics and these national own-price coefficients. Whats really important in this model is that the demographic is telling us something here. The question is, what is the demographic telling us? Suppose we go back and look at these national own-price coefficients and compare that to the demographics. What kind of effects are we seeing here? And is there any way to explain them? In this case, the percentage of elderly as the number of elderly increases, the mean for this gap is going to be -41.29 and the standard error of this gamma is going to be 23.65. As the percentage of elderly increases, my old price elasticity is going to get more negative. In terms of price sensitivity it means that people are going to be more price sensitive. If I look at some of the other things, like college education -- it's difficult to say, ethnicity is being more family size, larger families, working women, more tends to be more price sensitive stores, fewer houses with a value of 150 thousand, bigger dominant stores relative to the competitors, in closer competitive distances to my store. If I had asked you in the beginning about what characteristics a price sensitive store has, presumably you would have agreed with me that it's going to be city stores. And essentially I'm picking up the fact that it is this lower income effect, that's what these results are saying. It's more elderly, more ethnic consumers, larger families, larger households, smaller household values or less wealth and these competitor effects are driving, following competition away for the less price sensitive; the closer the competion is the more price sensitive the store is.
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