Taxes: How the Pandemic Ruined My Tax Results


IMPLAN is always striving to improve our methodology and in 2018, we made improvements in the processing of TOPI. The changes include using net TOPI payments (including subsidy payments) instead of gross TOPI payments to both federal and state & local government Institutions. This change is unambiguously an improved methodology, providing a more holistic picture of an industry’s tax payments. These improvements happened before the 2020 pandemic as part of IMPLAN’s general process improvements and resulted in mostly small changes to models that went largely unnoticed by the general public. Until now.



During the data production process for the 2020 data, the federal Paycheck Protection Program (PPP) made monstrous payments directly to businesses. These subsidies had an unexpected effect on how things look in IMPLAN.

In IMPLAN, the PPP payments are represented by negative payments from TOPI to the federal government. While negative TOPI payments are common for individual industries which are heavily subsidized (like agriculture), net negative TOPI payments for an entire Region is an extremely rare occurrence. However, during 2020 many Regions were net receivers in terms of TOPI payments to the federal government because of the PPP. 



When TOPI payments are negative at the regional level it can have some unintuitive implications. This is most easily seen in an example. Let’s assume that the entire region pays -$100 in TOPI payments to the federal government, but $200 in TOPI payments to state & local governments. In this case, the net TOPI payments to the federal government are -$100 + $200 = $100. Then, the federal percentage of net TOPI is -100 / (-100 + 200) = -1 and the state percentage is 200 / (-100 + 200) = 2. 

Now let’s consider an impact analysis in which the total impact to TOPI for the event is $10. The best practices of Input-Output would use the TOPI ratios above to yield TOPI payments to the federal government = $10 * (-1) = -$10 and the TOPI payments to state & local government = $10 * (2) = $20. The interpretation of this is that the Industry overall is a net receiver of TOPI, thus an increase in the Industry’s production would be accompanied by further negative TOPI payments (e.g. subsidies) as is the average in the Industry.

This can get even more unintuitive if you consider an impact to an Industry that is a net receiver in a Region that is also a net receiver. Let’s consider the same example above except that a positive Output shock would have produced a net TOPI payment of -$10. In this case, the federal government share of TOPI would be $10 and the State & Local share would be -$20. So, in this case, a positive Output shock will produce a correctly-calculated positive TOPI payment to the federal government and a correctly-calculated negative TOPI payment to state & local governments.

The interpretation of these Results and this issue at large is a bit murky. The literature does not have any examples of this type of payment structure in the SAM, so it is not clear what the best interpretation would be. 

It is worth noting that the overall amount of TOPI paid is unaffected by the distribution of those payments to federal and state & local governments. That is, the total amount of TOPI paid for a given Event is generally unchanged by this regional anomaly, but the distribution of those dollars is potentially problematic for some studies.

As for what all of this means for the IMPLAN User, there are several options:

  • If you are estimating an impact that occurred in 2020 and/or should include the activity of PPP loans and their inclusion in TOPI payments, then you should use the 2020 data and results should be unchanged.
  • If you are estimating an impact based on a year other than 2020 OR if PPP loans and their inclusion in TOPI payments are not part of the economic shock you are modeling then you can:
    • Use a different data year (likely 2019) that does not include the PPP loans in the source data. Seriously, pick any other year.
    • Use the ratios of federal and state & local government from previous IMPLAN data or outside data sources to redistribute the total net TOPI payments. In our above example, the user could simply take the $10 (or -$10) TOPI impacts and redistribute them using ratios from a different region, data year, or data source.


The 2018 switch from the now retired IMPLAN Pro is when this methodology improvement was introduced (along with a whole bunch of other cool stuff). TOPI is not net of subsidies in Pro. Therefore, this change means that the TOPI processes of IMPLAN Pro and IMPLAN differ, and so will your results. Check out the article Ready to Leave Pro.



2020 Data Release Notes

Pandemic: Analyzing the Economic Impacts of the Coronavirus

Pandemic: Additional Considerations when Modeling the Coronavirus



A $22 Billion Loss: The Potential Impact of Coronavirus on Foreign Travel to the US

Analyzing the Economic Impacts of the Coronavirus



Analyzing the Economic Impacts of the Coronavirus


Special Thanks to Product Director Drew Varnado for writing this article.


Written March 3, 2022

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