SAM LPC versus User Defined LPC
Good morning,
I'm working with several manufacturing (e.g., 329) sectors, and have a good idea of the amount of goods that would be purchased locally. However, when I run the model (with margins) with the SAM LPC of 7.3 percent, I get a much higher result than if I use the same model setup but change the LPC to a "user defined" value of 7.3 percent. I also tried it at a 100 percent, recorded the values, and then at a "user defined" value of 7.3, which resulted in about 92 percent reduction in impact values. But again, if I use the "SAM LPC" of 7.3 percent, I dont get anywhere near a 92 percent reduction. Why is this?
Thanks,
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Hello, It sounds like you are applying Margins to a producing Sector in the Model. If this is the case you want to use the LPP = SAM Model Value because this is demonstrating the correct behavior. You can see this by going back into the Margin screen ( Event Options> Edit Event Properties>Margins> Edit screen). The software is correct in setting the value which is attributed to the manufacturing Sector to the expected LPP value (7.3%) but the remaining portions of the Value Chain are given their appropriate values (based on Margin distribution) and their appropriate LPP values based on the local purchasing behavior of the Model. When you use the User LPC, you incorrectly force every element of the Value Chain to have the same LPP as the manufacturing Sector. Because applying Margins splits the purchase across the elements of the Value Chain and each element of the Value Chain will have it's own SAM Model Value, you will get a 'mixed' results not equal to 7.3% from a correctly applied LPP and Margined Model. You will also be able to see in the Detail Results > View By: Output, sorted for Direct Effects that this because Margining splits the Industry Sales values across the contributing Industries. If we have misunderstood the issue, we apologize, it would greatly help if you could give us additional details about the other Sectors you are using, The Activity Types you are using, and the region you are building in, including the year of the data set. Best regards, IMPLAN Group Staff
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