Dear IMPLAN, I'm working on an industry contribution analysis of the grains sector and am getting very high employment multipliers in my results. Initially, I was working with a model where the ag sectors were modified to match state-level output and employment, value added, etc. Given the multipliers I was getting (roughly 5x total employment compared with direct employment ), I went in and changed jobs numbers for most industries where the employment was significantly different from BLS QCEW data (I'm working with a 2014 Arizona model). This only helped a bit and brought it down to about 4x total compared with direct. Out of curiosity, I ran a clean, un-modified state model for the grains sector using standard procedures for an industry contribution analysis, and got a ratio of 4.37x for total jobs versus direct jobs. Looking at multipliers in the model, there are a few industries with very high employment multipliers, but most are in the 2-3x range (total compared to direct). Besides modifying total jobs in other industries that would be significantly connected through indirect and induced effects, are there any other strategies to bring the total jobs multiplier in line with what I see in the literature for the grains industry, which tends to range between 1.4x to 1.9x at the state level? Thank you, Dari Duval
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