Making sure not to double count mining jobs

Hello Donovan, It seems counter-intuitive, but a sector’s multiplier does not depend on the sector’s overall size; just on the relationships (e.g., output per worker) of that industry. For example, suppose the output and employment for the grain milling sector were $1 million and 100, respectively. The output per worker is then $10,000. Then, when you run a $1 million impact on the sector, you will get 100 workers. If, on the other hand, you reduced the grain milling sector’s output to $500,000 and re-balanced, the employment would be reduced to 50 and the output per worker remains the same at $10,000. So if you now run a $1 million impact on the sector, you will still get 100 workers Since impacts don't affect the Study Area data, there is no concern as regards what is already represented in the Study Area data. We would recommend creating two Activities, one for the pre-production activity and one for the production Activity less the pre-production Employment. You can then address the two combined Activities as the continual production level impact, and still describe the pre-production level portion. Let us know if you have more questions.

I believe that you are correct, but only for the direct impacts. For the indirect and induced impacts the study area connections will have changed by customizing (an thus reducing) the jobs in the study area data in the industry that we are modeling. By reducing the employment in an industry, we will also be reducing the indirect and induced jobs that were associated with that industry. When the model is then reconstructed all the way through the multipliers, some of the connections that used to exist will be gone. If we then set up a scenario where we add back more jobs then we took away, the same connections are not reestablished. In fact I have done this for a comparison. I took away all of the jobs in an industry except 1 by customizing the study area data. This way the industry does exist in the model but there is almost no one employed by the industry. I then set up a scenario where I added far more jobs back into that industry than previously existed and compared the output to the same scenario just described except that I never altered the study area data (the industry in question remained the same as it initially was presented by IMPLAN). The result was a drop in the indirect output of more than 50% for my scenario. Intuitively this makes sense to me as described above. Since the local economy no longer has the same connections that it had before it was customized, the model is forced to go outside of the local area to satisfy the needs of the new scenario (indirect and induced outputs). Of course this also does not make sense to model things this way since we know that the industry does exist with many more people employed than 1. At this point it seems more realistic to model the area with the jobs already present and risk some double counting. We certainly do not want to under-represent the area's ability to support these jobs.

Hello Donovan, We apologize that we weren't clear, but what we were trying to indicate in our previous post is that there is no need to, nor do you want to, customize the Study Area Data. The reason for this is exactly as you have stated; if you change the Study Area Data , you alter the RPCs and as you have suggested this is not desirable. What you may want to change are the Output per worker and Labor Income per worker ratios in the Event if you have enough information to do so. These can be adjusted by entering your known production value (industry sales) and then modifying employment to match your known total jobs. You can also adjust compensation and Proprietor Income to known values. This will trigger little exclamation marks, they are just indicating that you have adjusted the underlying relationships. This method should resolve the issues that you are seeing when you customize the Study Area Data. You will not be double-counting anything if you run your impact on the industry in its original state. Please let us know if you have any further questions.