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Full Video Transcript: 

Hello. I'm Jenny Thorvaldson, Chief Economist and Director of Data Development here at IMPLAN. In this video I'm going to show you some of the IMPLAN data -  how we can dig into the data a little bit and see changes over time. In particular we're going to look at changes between 2019 and 2020 using IMPLAN data.

Of course, changes between those two years are of a lot of interest because the economy changed so drastically that year, largely due to the pandemic. I'm not claiming that every single change that we're going to see in the data is due just to the pandemic - someone would have to do some statistical analysis on that - but you can do that with IMPLAN data.

In this video I'm just going to showcase some of the changes and how they jibe with what we heard in the media and in other reports and show you a little bit where to find those in IMPLAN and hopefully spur your interest in doing your own research. Looking at changes over time, differences between geographies, and things like that.

So I'm going to go ahead and share my screen and start my presentation and we'll be hopping into this presentation and then into an Excel spreadsheet that I have to showcase some of the data. And then at the end we'll hop into the IMPLAN tool itself.

First, let's look at changes in the number of proprietors.

In IMPLAN we have data on two types of employment. 

We have proprietors and then we have wage and salary employees. And those employees can be employees of proprietors or of larger corporations as well.

Let's start with the proprietors first. While we know that many existing businesses shuttered during the pandemic, there were also a lot of reports of new business formation.

According to the National Bureau of Economic Research, there was an article they put out about that business formation surged during the pandemic.

The U.S. Chamber of Commerce put out a report that startups during the pandemic have skyrocketed.

And NPR had a report that the period from March 2020 to June 2021 marked a record high for business startups.

A lot of those are going to be proprietorships. 

Let's open up our Excel spreadsheet. In the first tab here I have proprietor employment. This is from IMPLAN data models and we have our 2019 number and our 2020 number, and indeed we gained almost a million proprietors for just over a 2% increase in proprietors.

So that's kind of interesting. And then we could also look at the change in proprietors by state.

So here are a couple articles and reports about Florida.

Inc. said entrepreneurs are flocking to Florida. And the Miami Herald said that south Florida is number one in the US for new startup activity.

So let's pull our spreadsheet back over. We can look at this in this tab. I have information on proprietor counts between 2019 and 2020 by states and I have it sorted by the actual number of change, not by percentage, but you'll see that Florida is the top state for the number of new proprietors in the state. But as I scroll down, you'll see that 5.9% is also the highest percentage followed, it looks like, by Delaware here with 4.6%.

So this does seem to jibe with those reports that Florida is having a lot of startup activity. But I did want to point out that even though the U.S. as a whole increased its number of proprietors, there are a couple of states, a few states that actually experienced a decline in the number of proprietors. The two states with the highest percentage loss of proprietors were Alaska and West Virginia.

And this wasn't particularly surprising to me being familiar with the data and knowing that Alaska and West Virginia both are pretty mining heavy.

And knowing that mining is, as an industry whole, is very proprietor heavy.

There are a lot of proprietors in mining sectors compared to other sectors - agricultural sectors as well.

So I dug in a little deeper and I pulled the data specifically for Alaska and West Virginia.

And just looking at the top industries that lost proprietors between these two years in these states.

And sure enough, in Alaska look at this, you can see a lot of mining and you can also see some tourist related things which is, of course, not surprising with a pandemic.

So air transportation, scenic, sightseeing transportation support, other amusement, and recreation industries.

So those lost proprietors as well. So they're not super surprising.

And in West Virginia here again we see a lot of mining and there we see some agricultural sectors and, as I mentioned, those are pretty proprietor heavy sectors. So it makes sense that those sectors lost some proprietors.

Okay, let's see what else we can look at now.

Why don't we change and look to changes in wage and salary employment.

These are ones that you're going to hear in the news more often probably than proprietors. We all know that there was a significant loss of wage and salary employment in the US during the pandemic. Of course we still are in the pandemic. But in this first year of the pandemic we lost a lot.

So according to the Economic Policy Institute, low wage, low hours workers were hit the hardest during the recession. And according to PBS, the pandemic worsened the childcare crisis

Let's see if we can see some of that, those kinds of trends, in the spreadsheet we have here.

So I'll hop over to wage and salary employment in the U.S. Now before you get overwhelmed by this spreadsheet let's just focus first on this left hand side. And I will say that both of these sets are the same data. I'm just sorting them ascending and descending.

So first let's look at these totals here. In row two, we can see that indeed the US did have a net loss of wage and salary employment between these two years according to the IMPLAN data.

Now even though there was a net loss, not every industry itself lost employment - wage and salary employment.

So that's why I've sorted them differently and I used different colors to highlight a few things that make intuitive sense.

And again, I have this sorted by the actual value in change so not by the percent change but I'm highlighting some of the percentage changes here.

So it's pretty intuitive that we all know that restaurants lost a lot of employees. So here we have full service restaurants, all other food and drinking places rising to the top. Hotels and motels. So basically you know dining and lodging and sort of leisure, hospitality - tourism-type things that are not unexpected. 

Some education - you know the teachers are having a hard time wanting to teach when there's COVID and their jobs got a lot harder.

And then retail, we know, brick and mortar stores really suffered quite a bit through this. Gyms were closed down for a while. There's that child care services that there was an NPR article about - indeed they lost employees.

All of this is pretty intuitive here for the ones that were lost.

Now if I scroll over and we look at sectors that actually gained employment over the period, a couple things to note. One would be, warehousing and storage. Of course, that makes a lot of sense as folks are ordering more things online. We've all heard about Amazon warehouses being built - things like that.

And then here's one interesting one: food and beverage stores - so that's grocery stores. They saw a little bit of an increase. Of course as people are eating less at restaurants, they're eating more at home. So we did get, you know, a boost it looks like, in grocery store shopping.

And then another one I wanted to highlight was that while on this left where we saw all these losses and all these retail sectors, these other brick and mortar retail over here on the right side, we saw a gain in the grocery store retail, but then also in non store retailers. And that is where online shopping largely fits in.

So again, pretty intuitive stuff there - but kind of interesting to see by industry what's going on.

We could also look a little bit by state. Remember this time we're looking at wage and salary employment.

So I'm just pulling together some news articles I found and seeing how the IMPLAN data reflect those.

We hear from the Economic Policy Institute, no matter how you measure it, leisure in hospitality were the hardest hit in the pandemic downturn. We saw some of that. when we were looking at the U.S. data just a moment ago, but we're going to look at it statewide as well and see how that matches up. 

U.S. Travel Associations said leisure and hospitality experienced triple the job loss compared to the next hardest hit industry. This slide is really focusing on leisure and hospitality.

Well, let's take a look at wage and salary employment changes by state.

Over here on the left, I have it sorted - this time I have it sorted by the largest percentage change and what pops up - Hawaii - and we all know that Hawaii is very tourism dependent. So that's not a huge surprise to any of us, and especially given those reports in the previous slide.

I poked a little deeper into Hawaii and looked at the sectors with the largest change, the largest drop in wage and salary employment, and there you will see a lot of those leisure and hospitality sectors. 

We have restaurants, hotels, motels, other food and drinking places. We've got retail. We have scenic transportation, we have gambling, travel arrangements and reservations services. So almost all leisure and hospitality here and tourism based things other than that we have again, our teachers in public education.

Alright, so again, IMPLAN data seem to reflect reality what's going on here.

Okay, so we've looked at for proprietor employment, we've looked at wage and salary employment. What else might be interesting?

I was thinking foreign trade because we heard a lot, we've heard about the supply chain issues. We've just heard about exports shutting down to cut back on transmission of the virus. Certain countries didn't want to trade as much and and things like that.

So let's have a look at that.

A couple of reports here. We have the United Nations Conference on Trade and Development had a report that there were large declines in international trade in 2020.

And the World Trade Organization was predicting that trade was set to plunge as the pandemic continues.

Let's see what IMPLAN data on on trade show us. Let's bring up our spreadsheet in my foreign trade tab here. And sure enough, we have foreign exports falling by about 16% and foreign imports falling by about 11%.

In IMPLAN, you could go in and look at the commodity detail on this as well to see which commodities fell the most in terms of foreign exports and foreign imports. I'll leave that to you. I'm just giving you a little showcase here in this video. But sure enough, IMPLAN data do reflect what these folks are reporting.

Another thing that might be interesting to look at is household spending. That one gets a little trickier without digging deeper into some things, but let's just look at a couple of reports and see how the IMPLAN data reflects that.

According to the Bureau of Transportation Statistics, total spending by American households fell in 2020 with transportation spending dropping the most significantly percentage-wise.

And then the Federal Reserve Bank of Kansas City reported consumer spending declines and shifts in response to the pandemic. So not only did consumer spending fall, but what we're spending money on also changed.

Let's see what IMPLAN has to say about that.

Let's look at our household spending. Let's first, as always, focus on this left bit of data here. And sure enough right here that I have highlighted, we see that total household spending in millions of dollars did fall between the two years. 

Now. that in and of itself is interesting but what if the number of households increased in the US, which indeed they did, then we really want to look at household spending per household.

Here we have the household count increasing a little bit and then if I calculate down here, household spending per household, then percent change is actually greater - it actually dropped by over 5% because spending fell while household counts increased.

We indeed see the household spending fall. 

Let's look a little bit at the commodities that changed and that's what they were talking about where consumer spending fell, but it also shifted to different things. Often in recessions, it's very typical for spending on meat to decrease.

Meat is one of the more expensive food items and it's not necessary to eat meat.

So those are often first to go and sure enough we see quite a drop in household spending on meat.

And so that's kind of interesting. 

Again, we see increased spending in non-store retail. So that's online shopping. Sure enough, got quite an increase.

You see a decrease on some transportation, especially scenic and sightseeing transportation, which is not surprising.

I'll make this a little smaller so we can get all our columns in here.

One interesting thing I noted, there was a little bit of an increase in household spending on truck transportation and I'm assuming that has something to do with increased shipments to homes now that we're shopping more online. Of course, I'd probably want to dig into that a little deeper. Just to give you a little taste of what we can see, that households really did shift their spending to different things and decreased it overall.

Oh, here's one, this particular sector - polish and other sanitation goods - is where hand sanitizer and disinfectant wipes fall, and so there's another intuitive one and that's one that increased, so kind of fascinating stuff.

And then just a few other changes. One would be population that might be of interest to folks. I was thinking not so much population of the US as a whole, but shifts in where people live because there was a lot of moving around as people could work remotely and things like that and other reasons for moving - cost reasons, cost of living, job changes, what have you.

So I pulled the state level data for changes in population and, I wanted to see some trends and I have these ordered by percentage change, and Idaho experienced the largest percent change in its population over the period, which isn't totally surprising.

You'll see a lot of the Rocky Mountain West states here, and Southwestern states  towards the top here.

And this jibes with news reports about population increases in that area, and Texas Florida, and South Carolina are in there.

So that's kind of interesting and then a few states experienced a little bit of a net loss, including New York, Hawaii, and Illinois, so kind of interesting. I can attest, I see a lot of Illinois plates here in Colorado so it seems to jibe with my anecdotal experience.

A few things that other folks might be also interested in would be wages per worker.

Now this one is fascinating and I didn't want to pull data for it because it's super nuanced. If you recall, you might have heard reports that wages per worker actually increased in 2020 in a lot of sectors.

And this was surprising to people and that's why it's kind of a nuanced measure because it's not that workers were getting raises and workers were earning more themselves. It was that, if you remember that report I showed in an earlier slide, that the lower hours, lower wages jobs were the ones lost more proportionately.

We lost a lot of lower wage earning jobs and so the jobs that remained were just higher earning jobs - it's not that those jobs started earning more, just that they continued to exist more so than did lower wage jobs. This one's a little trickier to parse out. 

Similarly is productivity -  output per worker. If a lot of industries cut down on workers and either have their remaining workers work harder, work more, work longer hours. or the transition somewhat to robotics or what have you, productivity could increase in a lot of sectors and you may want to look at that, but there's a little bit of a note of caution there for industries whose products experience large price swings from one year to the next. So for example agricultural commodities can change in prices a lot. Things like oil and gas and refined oil. Those prices change a lot and output is the value of production. And so output for sectors like that could change a lot even if the number of physical units stays the same. So if the price of refined oil goes up but we produce the same amount of refined oil in the U.S. output is still going to go up.

So productivity needs to be really interpreted with all that knowledge behind it and make sure you look at prices and all those things to know whether actual productivity increased or not.

A couple of things that might be of interest to folks is looking at changes over longer periods of time and changes at a more local scale - you can do both of those things in IMPLAN and I'm gonna hop in there and show you just a little bit of that. At IMPLAN we have data dating all the way back to 2001 and they're all in the same sectoring scheme, rerun using our current best practices, so they can easily be compared over time. That's where you can do those statistical analyses if you'd like.

And then we also have data at a more local scale. I have shown you some of the state data but we also have data at the county level and MSAs which are groups of counties, zip code level data, and congressional district data.

So where do we find those data in IMPLAN? I'll just hop into IMPLAN.

We have not only the impact tool which IMPLAN is most known for and most used for, you can also just explore the data for your model.

So I built a model of the Rocky Mountain West and here you can see the states that I put in there as Rocky Mountain West and in the regions overview you can see things like gross domestic product, total employment, number of households, population, all those kind of things.

But in this study area data is where you can really get into more detail.

We can look at the industry summary.

I'll actually go into industry detail and you'll see the different types of employment.

We have wage and salary employment and what they earn in employee compensation. Then we have proprietors and what they earn in proprietor income.

So really just in your standard IMPLAN economic impact model, you can find a lot of the data that I showed you today.

Now, we also have in IMPLAN something fairly new called Data Library.

You can we have all these years of data. In Data Library is where you can access all those years of data and you can see trends over time. So here I'm looking at California's wage and salary employment over time.

This isn't by industry but you can look at it by industry in here and you can see our drop off here in 2020, and as well a drop off back here after the financial crisis.

This is another place where you can really delve in and and compare regions and look over time and you can go down to county level here and things like that.

And I thank you very much for joining me on this video and we'll see you in the next one.

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