The 2020 Data Year is now available in the IMPLAN application! As you can probably imagine, this Data Year is going to have some unique caveats that will reflect the state of the national economy during the global pandemic.  This article will cover some of that weirdness that is evident in the new dataset, why that weirdness is there, and considerations for using it in your economic impact studies. 



There were two main forms of government stimulus in 2020. The first was the stimulus checks sent to individuals and families coupled with expanded unemployment benefits. Some of this money was spent and some was saved. The levels and types of spending and saving in the Household Social Accounting Matrix column will include what Households did with the stimulus and unemployment benefits. Changes in the relative amounts that Households spend among the various commodities is also captured in the 2020 data (e.g., more on grocery stores, less on restaurants). 

While gains in Household Income due to stimulus checks and unemployment benefits will be captured in the underlying data in the new dataset and are accounted for in the relationship of spending per dollar of new income, IMPLAN will not assume households receive more payments from these sources in your analysis because government payments are not by default internalized in the IMPLAN multipliers. If you would like to analyze additional gains in Household Income, these gains can be analyzed using a Household Income Event or by modeling the specific spending as individual Events.

Note, even with the fiscal efforts to support household spending, there were exceptionally high personal savings rates in 2020 as people spent less money due to quarantine and concern over less economic security. The increased savings rates will be applied to income analyzed in IMPLAN. So, all else equal, this will lead to lower Induced Effects.



The second government initiative was the Paycheck Protection Program (PPP). As this money was treated as subsidies for Industries, it shows up as reductions in TOPI. Remember, TOPI is reported as a net of subsidies. The decreases in TOPI were offset by an equal increase in OPI, which explains why some Industries see losses across all pieces of Value Added except for OPI. This OPI is the infusion of the government money that is to be used to cover operations and maintain employment levels. All else equal, when a firm gets a subsidy, it has a negative effect on net TOPI and a positive effect on OPI. For more information on this, check out the BEA. Again, read the cautions below on reporting tax results.



For some Industries, there are very high losses evident from anecdotal evidence as well as the 2020 data. We expect to see decreases in restaurants, travel, and amusement activities. Decreases in Output, Value Added and its components, and spending on Intermediate Inputs frame the story for each Industry. Many Industries were highly subsidized already, and then added additional monies through the PPP. Many Industries had to shut down (at least temporarily), decrease workforces, or pivot their entire business model. Many spent less money overall on Intermediate Inputs and a few saw international export markets dry up. All of these moving pieces need to be brought to mind when examining the losses seen in the data.

Regions and states that put stay-at-home orders in place earliest will show the largest losses. So if your county or state was quick to shut down, your Results will be more dramatic than other areas.



Because TOPI is net of subsidies by definition and the second quarter of 2020 saw excessive subsidies due to the CARES act, TOPI values in IMPLAN will be lower in many industries than they would have been otherwise. Thus, these subsidies are correctly accounted for in the IMPLAN Regions data.

However, this correct accounting in the Regions data can have undesired effects on the tax impact results. Because Federal TOPI has gotten smaller due to these subsidies, the ratio between Federal TOPI and State/Local TOPI has fallen. Thus, when calculating tax impacts, the model divvies up TOPI among these two levels of government using this smaller ratio, Federal Government is given a smaller share of any TOPI impacts and State/Local Government is given a larger share.

The consequence of this is that when the TOPI impact is positive, State/Local TOPI impacts will be overstated (too large a positive) relative to Federal, and when the TOPI impacts are negative, the State/Local TOPI impacts will be understated (too large a negative) relative to Federal. Thus, the magnitude of State/Local Government TOPI impacts will be overstated relative to Federal TOPI impacts in either case - being either too positive or too negative.

While State/Local TOPI actually stayed relatively stable, the share of these of total TOPI looks to increase in the data. Therefore, we recommend using your own tax estimates for the local and state levels or using tax estimates based on the 2019 Data Year in lieu of the 2020 Data Year.

For more information on how the PPP affects the BEA NIPAs, please see this article from the BEA.



When using the 2020 Data Year, always add as much information as possible to your Industry Event in the Advanced Menu. This process is now easier than ever with the addition of the new Industry Impact Analysis Event Type (IIA) in IMPLAN V6! With the IIA Event Type, you can specify any value in an Industry’s Leontief Production Function (LPF) as your event value. With the IIA Event you can also customize Industry Spending Patterns. Note that this Event Type means that it is no longer necessary to utilize Analysis-by-parts in order to input those values. 

Referencing an industry’s Leontief Production Function from 2019 can be helpful if an Industry’s production decreased during the year and their LPF changed in a way that does not reflect their operational LPF. For example, if Employment has decreased in an Industry, particularly among their lower wage employees, the Industry’s Average Employee Compensation (EC) likely increased and will potentially lead to an overestimation of Direct EC in an impact analysis. In reality, that Industry will probably need to hire for the positions lost to ramp up operations again.

Consider adding a footnote to your report or presentation that notes not only which dataset was utilized, but also includes a disclaimer about the potential implications of using it.

Run your analysis using the 2019 Data Year and the 2020 Data Year to see a range of the potential economic impacts. If the economy shifts back to a pre-pandemic state, this may help estimate future impacts. Although, our economy may never return to the “old normal” and it is unclear how the recovery will look. Brookings outlined some potential options that are worth considering when modeling your impact.

Finally, if you’d like to venture into painting your own picture of the current or future state of the economy, you can make edits to Industry levels and per-worker values in the Study Area data by Customizing a Region



Running an Industry Contribution Analysis using the 2020 data is a great way to see what the effects of a business or industry are in the COVID economy.  Run your analysis at a 100% contribution to estimate the effects supported by an entire Industry. If you are running just a firm or business, use the current level of Output they produce. 

Comparing a 2020 Industry Contribution to a 2019 Industry Contribution is recommended for estimating the before and after effects of COVID-19 (keeping in mind it's unknown what changes we still might see). However, remember to note that the changes in taxes between these two Data Years will be dramatic because of the large subsidies in the 2020 data.



Using the annual 2019 IMPLAN dataset might still be the best option if you are trying to model anything before the pandemic or any events occurring in 2021 or later. We always recommend using the Data Year that looks most like the year you want to analyze. Because of the uniqueness of the 2020 Data Year in IMPLAN, it is possible that this data does not accurately reflect the economic conditions you are trying to model. So keep that in mind when deciding whether or not to utilize the 2020 Data Year in your analyses.  



The 2020 annual data set incorporated National, State, and Puerto Rico population numbers from the 2020 Decennial Census rather than from the usual estimation source. County data was not available at the time of completing demographics. Population data used comes from census apportionment tables, taken from the Census, published April 26, 2021.

Our county population numbers for 2020 come from the U.S. Census Bureau's Population Estimates series, which is the same source used for any regular IMPLAN data set.



Valdez-Cordova Census Area, Alaska (State FIPS 02, County FIPS 261) was split to form Chugach Census Area (County FIPS 063) and Copper River Census Area (County FIPS 066), effective January 2, 2019.  This change will be reflected in the IMPLAN annual data starting with the 2020 annual IMPLAN data.  



As we are all aware, the U.S. (and global) economy experienced significant changes in 2020 associated with the Covid-19 pandemic. The 2020 IMPLAN data will reflect many of these changes. With very few exceptions, any lagged raw data are controlled to non-lagged data points at more aggregate levels.  IMPLAN data are not adjusted to be smoother over time; rather, our goal is to publish data that are as accurate as reasonably possible.

We understand that a large shift one year may not reflect the long-term average for an industry, and therefore may pose difficulties when running impact analyses. Some suggestions and considerations, each of which depends on the specific circumstances and goals of the analysis: 

  • The Industry Impact Analysis (Detailed) analysis type, allows full customization of the direct effects when setting up an analysis, wherein one could use a several-year average for some values, if such customizing is appropriate and documented.
  • A comparison across industries or geographies may be useful in highlighting regional or industry-group strengths relative to other regions or industry groups.  Similarly, an individual business or project may have fared well relative to the overall economy in the region or nation. 
  • IMPLAN’s new Data Library contains IMPLAN data dating back to 2001, projected quarterly data, and tools for comparing regions and industries (location quotients, shift-share, etc.), all of which can help to provide a richer context to any analysis or report.
  • Detailed information about the IMPLAN data production processes, educational articles and videos, expert Q&A, and more support for your analysis can be found From the Data Team.
  • You can also check out some considerations for analyzing your impacts with the 2020 dataset as outlined in Using the Evolving Economy - COVID 2020 Q2 & Q3 Data.



BEA Input-Output Accounts

NOAA Commercial Fisheries Landings

U.S. Census Commodity Flow Survey (CFS)

U.S. Census Relationship Files

U.S. Energy Information Administration Residential Energy Consumption Survey


Written December 7, 2021

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