I coded the counties with a dummy variable (0 = interior, 1 = coastal) so that I could test whether there was
any difference in the 'mark-up' according to whether a county importing wheat was on the coast or not. A lot of wheat was shipped by coastal routes because that mode was often easier than using a horse and cart. I want to test for that. So
As you can see from the output below, all the explanatory variables are statistically significant at the 95% level (p < 0.05). The coefficient for distance is positive, meaning that the further away, the higher the price difference. In effect this is giving us the transport rate for hauling wheat. It should be positive, makes sense doesn't it? Surplus/deficit has a negative sign. That means that the more surplus the county has, the smaller the price difference. Again, that makes sense. Why would a county pay more for wheat if they already have lots of it? The coastal dummy is the most interesting. It has a negative sign. So if you lived in a county on the coast, you paid less for wheat "other things being equal". We are 'controlling for" distance and surplus. So if we happened to have two counties, both the same with respect to distance and surplus, the one on the coast would pay less for wheat. Because they got more of their wheat through coastal shipping would seem to be a reasonable explanation! Can you see the power of these techniques? We can learn a huge amount just with a few bits of data scraped off the floor.