The newly released Evolving Economy - COVID 2020 Q2 Data and the Evolving Economy - COVID 2020 Q3 Data is available in the IMPLAN application as Data Years COVID 2020-Q2 and COVID 2020-Q3. This article will cover selecting the new dataset and considerations for using it in your economic studies.
So we know that 2020 hasn’t been a typical year. The data for 2020 is no exception. Our Data Team put together an annualized dataset based on the second and third quarters of 2020: when COVID hit the world. So the entirety of the dataset looks at how the whole year would look based on these two quarters of the year.
The data is seasonally adjusted. Industries that employ more folks in the summer months are better represented in their employment for 2020. We also know that some of these Industries were the hardest hit with layoffs and furloughs: restaurants, airlines, and other tourism related fields, for example.
Due to the nature of quarterly data releases, outlined in the article Evolving Economy - COVID 2020 Q2 Data Details, the estimates for the COVID 2020-Q2 and Q3 data are not as reliable as those of the annual IMPLAN datasets. The values for Employee Compensation, Employment, Personal Income and Household Personal Consumption are based on detailed source data. The estimates for Output, Intermediate Inputs, and TOPI (see note below on taxes) are extrapolated from employment and productivity data, whereas OPI is estimated solely as a residual; accordingly, they are less reliable than the estimates of employment, compensation, and personal consumption. The values for OPI are the weakest in the data.
The good news is that the composition of household spending did change to reflect the adjusted spending behavior. This will affect the Induced Effects. Both the amount of spending and the items purchased are different in this dataset. Industry Spending Patterns and Institutional Spending Patterns also changed based on Output. For example, if there is less Output in restaurants, there will be less purchasing from restaurants by Industries and Institutions.
WHERE TO FIND COVID 2020-Q2 AND Q3 DATA
On the Regions screen, the current year will still be the default Data Year. If you want to use the new Evolving Economy - COVID 2020 Q2 or Q3 Data, you will need to select this dataset from the drop down menu.
You can run Events using the COVID 2020-Q2 or Q3 Data Year just as you would any other Data Year. You can also make this selection from the Data Year drop down menu with a Group on the Impacts Screen. Do note the cautions outlined below before analyzing your results.
STIMULUS CHECKS, UNEMPLOYMENT BENEFITS, & HOUSEHOLD SPENDING
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 money. Changes in the relative amounts that Households spend among the various commodities is also captured in the COVID 2020-Q2 and Q3 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 additional gains in Household Income are expected, 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 Q2 and Q3 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 maintaining 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 COVID 2020-Q2 and Q3 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.
COMPARING 2019 TO COVID 2020-Q2 OR Q3
The first consideration when using the COVID 2020-Q2 or Q3 Data is to remember that this is annualized based on what happened in the second quarter of 2020. The relatively normal first quarter is not represented in this dataset; it’s truly a look at the start (and hopefully worst) of the pandemic.
When comparing the 2019 data to the COVID 2020-Q2 or Q3 Data, you will see vast changes. Remember we are looking at a full year of complete data in 2019 compared with only the annualized activity from the second or third quarters of 2020. One cannot attribute all of the differences between the COVID 2020-Q2 and Q3 datasets and the 2019 dataset to the pandemic and associated government responses.
CAUTION WITH OCCUPATION DATA
Users should exercise caution when using IMPLAN Occupation Data tied to the Evolving Economy - Covid 2020 Q2 and Q3 Data. This is because there is no update to the most recent Occupation Data. The Occupation Data is from 2018 and will not reflect some of the dramatic changes that have occurred due to the pandemic.
One notable example is the Industry 502 - Amusement parks and arcades. The source data indicate that employment declined by 44% and earnings declined by 2%. This likely is due to a reduction in seasonal and hourly employees while full-time administrative and executive employment was maintained. The smaller decrease in earnings is because the year round employees earn higher incomes. However, when applying Occupation Data to the COVID 2020-Q2 Data, you will not see this change in the composition of employment reflected. So, it would appear as if everyone in the Industry saw pay rates nearly double, which is almost certainly not the case given what we know about how the pandemic has affected this Industry.
CAUTIONS WITH TAX IMPACTS
Because TOPI is net of subsidies by definition and the second quarter of 2020 saw excessive subsidies due to the CARES act, tax results are overestimated at the local and state levels. You can see this by examining the tax detail and seeing that all the growth is in TOPI, not in personal taxes. While local and state 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 COVID 2020-Q2 or Q3 Data Years.
ECONOMIC IMPACTS USING COVID 2020-Q2 AND Q3
Using the annual IMPLAN datasets might still be the best option if you are trying to model anything before the pandemic or what recovery might look like. We recommend using the Data Year that looks most like the year you want to analyze.
When using the COVID 2020-Q2 or Q3 Data Years, always add as much information (Output, Employee Compensation, Proprietor Income, and Employment) as possible to your Event in the Advanced Menu. Referencing an industry’s Leontief Production Function (LPF) from 2019 can be helpful if an Industry’s production decreased during the quarter 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.
You may want to consider using Analysis-by-Parts (ABP). ABP is the suggested technique for analyzing a firm or Industry that doesn’t follow the Leontief Production Function for that Region, and the necessary edits to reflect cannot be made in the Advanced Menu of an Industry Event. Since the Direct Effects and the first round of Indirect Effects are the most significant in an impact analysis, modifying your analysis using ABP will help you better reflect the current state of the economy.
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 COVID 2020-Q2 or Q3 Data Years 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.
ECONOMIC CONTRIBUTIONS USING COVID 2020-Q2 OR Q3
Running an Industry Contribution Analysis using the COVID 2020-Q2 or Q3 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 an COVID 2020-Q2 or Q3 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 occurred in 2019). However, remember to note that the changes in taxes between these two Data Years will be dramatic because of the large subsidies in the COVID 2020-Q2 and Q3 data.
Due to limitations with our legacy software, IMPLAN Pro, the COVID 2020-Q2 and Q3 Data is only available at the national, state, and county level in the desktop version. To see congressional district or zip code level data, you will need to be using app.implan.com. If you don’t have access to the new platform, email firstname.lastname@example.org today!
Written September 16, 2020
Updated February 17, 2021