A Net Analysis reports a holistic look at the effects resulting from a change in production or spending in the economy, both the positive and negative. Net Analyses that involve two different Industries will have net winners and net losers beyond just the two directly impacted industries. It can be useful to examine these sides in an analysis and can help create a truer impact picture. It is easiest to do this when you create multiple Events, including the positive and negative effects and analyze them in a Group defining the Region where these change will occur.
Pointed out in the IMPLAN Disclaimer, "I-O [Input-Output] models do not account for forward linkages, nor do I-O models account for offsetting effects such as cannibalization of other existing businesses, diverting funds used for the project from other potential or existing projects, etc. It falls upon the analyst to take such possible countervailing or offsetting effects into account or to note the omission of such possible effects from the analysis."
Net Output Considerations
Think about this example. When a new store opens up, local purchasing power doesn’t automatically increase to support it. Local residents and visitors are still likely to spend the same amount of their disposable income. These dollars must now, however, be split between more retail options. The new store will win in terms of selling goods, but the older stores will likely lose sales.
This is, of course, unless it can be argued that the new store is actually filling in “import substitution.” For example, if this was the first furniture store in the Region, people no longer have to leave to buy a bookshelf and therefore the money is no longer leaked out of the local economy. It is also possible that the new industry is increasing the region's base economy and will primarily be an exporter, meeting demand in markets outside of the region.
Net Employment Considerations
It is also a responsibility of the analyst to assess the capacity of the local workforce. In the example below, we will analyze a shift $100M in Output leaving the fossil fuel industry (-$100M Industry 40 Output Event) and being gained in the solar energy industry ($100M Industry 42 Output Event). IMPLAN will estimate the loss of fossil fuel jobs (71) and gain of solar energy jobs (495), and reflect the net Direct Employment impact (424). IMPLAN assumes this net Employment effect is local, but it does not consider if the 424 new positions will be filled by unemployed people living in the region, employed people from other local industries, new people moving into the region, or by an increase in workers commuting into the area for those positions. Instead, IMPLAN relies on an average regional commuting to determine the portion of new income that is earned by in-commuters. IMPLAN, as an I-O model, cannot dynamically determine how new jobs will be filled or cause turnover, potentially affecting employment and production in another areas of the regional economy. However, IMPLAN's Occupation Data is a great resource to supplement your knowledge of a regional workforce. For example, we might reference Occupation Data to research job placement options for the 71 fossil fuel workers who lost their job, and to evaluate the workforce that can fill the new solar energy jobs.
Barlow Energy of South Carolina is looking to move $100M in production from fossil fuels to solar. So we will see a decrease in fossil fuels at the same time we see an increase in solar operations. We want to look at the Net Analysis; the overall change in the economy because of both of these Events.
To set this up, we create two Events in South Carolina: a negative $100M in Industry 40 - Electric power generation - Fossil fuel and a positive $100M in Industry 42 - Electric power generation - Solar.
When we run this analysis, the Results screen will default to show us the net effects on South Carolina resulting from the change from fossil fuels to solar power generation - the net effect of both Events. We see that the Direct Output is $0 because we had both a negative and a positive $100M impact.
We can also see that this change will see a gain of 51.43 Indirect jobs and $17,105,205.66 in Indirect Output. The Direct Effects, aside from Output and Employment see decreases while Induced Effects and Indirect Effects all see increases; but why?
We can look at just the negative effects of the loss of fossil fuel power or just the positive effects of solar power by applying the Filter. If Barlow Energy wants to focus only on what jobs will be lost, we can Filter for our fossil fuels Event. Overall, this loss in $100M in fossil fuel energy would have a negative employment impact of 337.71 jobs. 71.45 of these jobs are Direct jobs lost from the Fossil fuel industry. 171.93 of these jobs are Indirect jobs. The remaining 94.34 jobs are Induced jobs.
We can see which Industries these negative job impacts are affecting by viewing the Detailed Employment Results. Notice we see the same negative Direct employment impact of 71.45 in the fossil fuel Industry.
If, on the flip side, they only want to show job gains from the switch, Filter for only the solar event. Overall, this gain in $100M in solar energy would have a positive employment impact of 826.79 jobs. Industry 42 - Electric power generation - Solar will see a Direct Employment impact of 495.55 jobs. The Indirect Employment impact is 223.35 jobs, and the Induced Employment impact is 107.89 jobs.
We again, can dig into which industries these jobs impact by looking at the Detailed Employment Results.
As a net effect, the switch will have a positive Effect on Indirect Employment because the employment loss from fossil fuels supply chain is smaller than the employment gain from the solar supply chain (-171.93+223.35 = 51.43).
The overall net change in jobs is 489.08 (826.79-337.71=489.08). We can therefore conclude that in South Carolina, investing in solar energy is better for overall employment than that of fossil fuel energy. Note that this might be a very different answer if we examined West Virginia where a significant amount of coal is mined or in New Hampshire where they have far fewer sunny days than South Carolina.
Written September 19, 2019
Updated February 22, 2021