I've got two related questions arising from an impact study of the distilling industry in Kentucky. This is the fourth study I've done, so these issues arise because the underlying input data have shifted for reasons I can't explain, and in this case lowered the multipliers enough such that the total employment impact of the industry using 2014 Implan data is lower than my previous study using 2012 Implan data despite a 14% increase in employment and a similar output increase in the last two years. I've looked under the hood and deduced that it seems to be the result of two issues: 1. A huge input in the distilling industry is product from the industry itself. My assumption is this is due to the fact that the Implan data for distilling includes all the corporate, production, warehousing, and bottling of the product under one industry category. I know that by comparing industry data gathered locally to Implan data. So everything that is bottled at a separate facility gets counted as an input. Also, small distillers often use the facilities or even purchase liquor from the large players so that gets counted as an input. At least that's how it makes sense to me. I've looked at the production function for the industry for every year of Implan 2009-2014 and while the distilled liquor input is pretty stable (34% to 38% of total inputs by value) the RPC for the distilled liquors input to the distilling industry is quite unstable and made a dramatic turn in 2013. The RPCs since 2009 have been .40, .60, .68, .89, .30, .27. My previous study was done with the RPC = .89 data. In 2013 a big new bottling facility came online and a couple others were expanded, so a drop in RPC might be expected, especially since much of the new product bottled was spirits brought in from outside the state. But the whole pattern before that, particularly reaching such heights, seems very suspicious to me (as well as the very low numbers for 2013 & 2014). Is there something with how these numbers are derived at that might explain the strange pattern of these RPCs over time? Was there a change in methodology? A trouble with data in one year? 2. The other factor depressing the employment multiplier using 2014 data versus 2012 is grain farming employment. Here's the Kentucky data on grain farming for 2009-2014: Description Employment Output Employee Compensation Proprietor Income Other Property Type Income Tax on Production and Imports 2009 Grain farming 18,179.70 $646,452,416 $19,222,018 $119,635,880 $121,420,000 $2,692,553 2010 Grain farming 17,852.00 $598,300,416 $22,971,960 $100,230,936 $10,882,737 ($36,868) 2011 Grain farming 23,980.10 $891,644,104 $30,718,813 $49,615,788 $198,613,907 ($70,725) 2012 Grain farming 23,254.00 $939,525,635 $24,524,286 $126,632,294 $84,444,244 ($25,325,521) 2013 Grain farming 11,514.70 $1,413,141,602 $16,599,262 $22,539,009 $48,234,634 ($88,794,266) 2014 Grain farming 6,982.20 $956,338,318 $18,313,168 $69,066,612 $177,668,365 ($116,549,690) Kentucky distillers currently buy about 50% of their grain inputs from instate farmers. As can be seen, employment jumps in 2011 and then crashes in 2013 and 2014 while output and the other numbers basically rise and fall with the harvest size. My previous study used the top end employment. Did the methodology change for calculating farming employment? Why the jump and crash? Any ideas? Changes in calculating seasonal employment? Any help appreciated.
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