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Timely Topics Webinar: A How-To on FRED

This webinar was recorded April 16, 2019.

FRED is a set of free tools from the Federal Reserve Bank of St. Louis that allows users to find, visualize, download and understand economic data, explains Keith Taylor, senior coordinator for the Research Datadesk at the St. Louis Fed. He speaks with David Benitez, coordinator for Government Relations at the St. Louis Fed, about FRED and provides examples that demonstrate the functionality that is available. Taylor also takes questions from the webinar participants.

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For more information about Timely Topics webinars, please contact David Benitez at David.J.Benitez@stls.frb.org.

Transcript

David Benitez: Good afternoon and thank you for joining us. My name is David Benitez. I am the Government Relations coordinator for the Federal Reserve Bank of St. Louis. And here with me today is Keith Taylor, Datadesk coordinator for the St. Louis Fed’s Research Department, who will be presenting on one of our best resources—Federal Reserve Economic Data or FRED.

So, briefly, this is our very straightforward agenda for today. I’ll go over some housekeeping. And then I will kick it over to Keith, who will give you the rundown on FRED, followed by Q&A, which I’ll explain on the next slide. And then, after that, we’ll conclude by sharing some resources and other opportunities for you to connect with the St. Louis Fed.

So, housekeeping. We will be recording this entire presentation, including the questions-and-answer period so we can provide it to you on our website at a later date. And then, speaking of Q&A, although we have an alloted time for questions, at any time during the webinar you can ask a question through the chat function. So, in your upper, there you go, right-hand corner, you’ll see two buttons—Participant and Chat. And I will try to get to as many as we can while still being respectful of your schedules. Additionally, after today’s webinar, we will be sending out a short survey. If you have a few minutes, we’d greatly appreciate your participation.

And then, finally, I just wanted to add that if you want to know more about the Federal Reserve and the St. Louis Fed, please visit our website at stlouisfed.org. 

Now, it’s time to kick it over to today’s presenter, Keith Taylor. Like I said earlier, Keith is the Datadesk coordinator in our Research Department and has been heavily involved in all things FRED for a number of years now. All yours, Keith.

Keith Taylor: Thank you very much. So, let’s go ahead and just start at the beginning for those of you who may not know what is FRED. FRED is a set of free tools from the Federal Reserve Bank of St. Louis that are going to allow you to find, visualize, download, really understand economic data. And this data is coming from a variety of places. We’ve got it at the U.S. regional level, the national level and the international level. And FRED is a one-stop shop. So, it’s going to allow you to compare data from multiple sources. So, you can pull in data from the Bureau of Economic Analysis, the Bureau of Labor Statistics. Combine these things together, visualize it, download it and really tell a story. All this can be found at fred.stlouisfed.org.

Most people when they think of FRED, they think of the kind of iconic blue graph that you can see in the upper left-hand corner there. But we can really do so much more. So you can make line charts, area charts, stacked area charts. You can change the colors, the types of the graphs, really make these graphs your own. We also have mapping because just some data looks better as a map. And we also have an Excel add-in. So, if you need a little bit more power, or you want to combine our data with data that you’ve produced yourself, you can pull it straight into Excel using our Excel add-in. 

At a high level, let’s just talk about the types of data that you’re going to find in FRED. We organize our U.S. national data into five main categories. So, we’ve got money, banking and finance; population, employment and labor markets; national accounts, which is also gross domestic product; production and business activity; and finally prices. At the U.S. regional level, we have data at the state, county and metropolitan statistical area level. If you’re not familiar with, like, an MSA, that is a geographic area of economic activity. They’re defined by OMB. They tend to correspond to the cities in our country. So, you can think of places like Washington, D.C., as an MSA. New York, Chicago, St. Louis, etc. And at the international level, we’ve got national data. So, you can make a comparison, say, of Chinese GDP to U.S. GDP.

And, as I said, FRED is a one-stop shop. So, we are pulling data from 87 sources, and we’ve got about 200 data sets that you can browse through. And you can think of a data set—we also sometimes call that a release. You know, some of the ones that you’re probably familiar with are, for example, the Bureau of Economic Analysis, and they produce gross domestic product, personal income, international trade, international transactions. We’ve got the Bureau of Labor Statistics, which is, of course, producing the unemployment rate and nonfarm payrolls. You’ll find that on their Employment Situation release. The consumer price index, the producer price index. And Census—we’re pulling a number of their economic indicators. Things like building permits, construction spending. And, then, we also have population data from them. And we also have socioeconomic data, which might be of real interest to you guys. And I’m going to go ahead and show you some examples of that in a couple minutes.

A couple of sources and releases that you might be interested in are the Congressional Budget Office. We pull the budget and economic outlook. And then we also get the reports of conditions and income for all insured U.S. commercial banks from the Federal Financial Institutions Examination Council.

Our international data mostly comes from the IMF, World Bank and the Organization for Economic Cooperation and Development, OECD. We also pull a little bit of data direct from the national statistical agencies for places like the U.K. and Japan.

One of the questions we get a lot is, “Why is FRED here in the St. Louis Fed?” Because we’ve got all this international data, national data. This is not just about the Eighth District. But we’ve got data for pretty much everywhere. And that comes, actually, from a research director in the 1960s named Homer Jones. And he had this, at the time, novel idea that said, “Why don’t we make data that the policymakers are using to make policy, and make it available to the public?” He wrote a memo and had a few example graphs in that memo. Sent that around to people in the Federal Reserve System. Everyone was like, “Yeah, this is a great idea.” 

That eventually became something called the U.S. Financial Data publication. We produced it weekly. Mailed it out to thousands of people. It was extremely popular throughout the ’80s and ’90s. And we actually still publish that online every week. As technology advanced, we started to update our data delivery methods. So, in the ’80s you could actually call into the St. Louis Fed and listen to recordings of interest rates. In the early ’90s, we set up sort of the beginning of FRED on an electronic bulletin board, which was like a precursor to the internet. And then, in the mid-90s, we moved everything to the internet. If you’re interested, there’s actually a pretty detailed history online.

So, just to kind of summarize at a high level, FRED is this one-stop shop, which is going to give you the ability to find, visualize, download all of this important data and really, hopefully, understand that data. It’s curated and managed by our team here. So, we are updating the data as quickly as we can after it comes out from the source. And we’re always adding new data. So, if we don’t have what you want today, let us know and we might be able to add it.

We also give you the ability to create content just one time and then reuse it over and over again through our free user accounts, which allows you to create saved graphs and also to create dashboards, which are basically a webpage full of visualizations, dashboards, maps, things like that that you can always return to. If you need more power, you can always pull in our data directly into Excel using our free Excel add-in. And, finally this is all coming from the Federal Reserve Bank of St. Louis, which is an impartial and nonpartisan source. All of this is free, and it will always be free. And I think it’s important to just reference that.

The best way, I think, to understand FRED, to learn about FRED, is actually to see it in action. So I think what we want to do is maybe just again start at the beginning here, just like you might at Google, and kind of work our way through.

So the easiest way to find FRED is just to make a search. So, you could search Fed FRED or just FRED. And we’re going to pop up right at the top there. Pretty easy to find. And then this is going to take you to the homepage. And there’s a lot here that allows you to find data and, also, to understand economic data. And then to also use our tools. So, I think it’s worth taking a moment to kind of walk through this before we dive into some data examples.

And, so, let’s just kind of work through this from top to bottom. So, again, at the top, we’ve got a user account. It’s very simple to sign up for one. Just give us your e-mail address and you’re off to the races. This is going to allow you to create custom content one time—and then it updates automatically—and return to it very easily.

We also have two search bars. These both work exactly the same way. Functions like a Google search. Type in some words. You know, GDP, unemployment rate, maybe your geography wherever you’re at—your county or state—and you’re off to the races in terms of finding data.

As I said, we’re a data aggregator. So, we’re pulling this data from 87 different sources. And we’ve got about a half of million times series. So, we cover a huge range of topics. And chances are we’ve got the data that you’re looking for.

A couple of other ways to search for data, beyond just doing the key word search, are by looking at the categories or the source and the release. And, so, if I click on this category, you can see here you can kind of dive in based on these high-level categories. And, again, these top five—money, banking and finance; population, employment and labor markets; kind of as we go down, etc.—those are all at the U.S. national level. And that’s going to be U.S. national data. If you’re looking for international data or U.S. regional data, you can dive into these two sections down here.

And finally we have some academic data, which has got some kind of interesting stuff made by economists. You know, it doesn’t rise to the same level as an indicator produced by an agency, but there’s some really interesting stuff down there as well.

And you can always return to the homepage just by clicking the FRED logo.

You can also dive in by source or release. So a source might be someone like the Board of Governors or the Bureau of Economic Analysis. And a release is the actual data set in which they’ve produced the data. So, if you’re familiar with that, you can kind of dive in this way, just by going through sources.

And, so, I’ll go ahead and click on the Board of Governors here and just give it a second. And now we’re looking at all the releases that we have that are produced by the Board of Governors. And you can click on any one of these. I’ll just maybe try the H.15 selected interest rates. And from here you can see we’ve got all of the series that are on the H.15, and you can really start working with this stuff.

At the top, we’ve got this link which takes you to another view of the data, which is kind of interesting. And you can kind of navigate through here. So, I can look at daily H.15 data. And here you can see all of these interest rates kind of spelled out in a table. And this is pretty close to the way you would actually find it if you went to the Board of Governors’ website. And, so, this is a really powerful way to look at data if you want to look throughout the release and kind of see how things are related to one another.

As I go down, I’ve got this beige area. And there’s quite a bit of stuff here that’ll help you kind of understand the data in FRED and also see what’s happening. So, we’ve got, of course, this FRED News section. So, we’re always adding data. So, we just yesterday added homeownership data with racial breakdowns. You know, pretty exciting. Anything, any new data’s going to show up there. We also, if there’s any time the data are not going to be updated for some reason, we’ll also post a note there. So, that’s a good place to kind of understand what’s happening.

We also have the FRED Blog, which is this really powerful tool for understanding economics and understanding how to use FRED. And, so, these blog posts are written by economists here at the St. Louis Fed. They’re published twice a week. And they’re these very short articles that just talk about—they always have the visualization—and then they talk about what you see in that visualization. So, it might be a graph. It might be a map. And they’re just kind of explaining the economics behind it. And then there’s always this section here on how to create the visualization, whether the map or the graph. And, so, this is a great way to learn about economics, learn about data, and also learn how to use FRED all at once.

And we’re also in the Research Department, so you can also see the latest research news, academic publications that we’re putting out—down here with the “Ahead of the Curve.”

Finally, at the bottom, we’ve got the At A Glance. So, these are the eight high-level series that look across the U.S. economy. From here you can very quickly get to, say, something like real gross domestic product and kind of take a look at that data.

We track what series people are looking at. And if you want to see what’s trending on our website right now, you can go to these popular series. And you can kind of see here. As things hit the news, this list changes over time. We’re also updating all the data, as I said, as quickly as we can. And, so, if you want to see what’s been released, updated most recently, you can check out all of these under the latest releases.

And, then, I talked about tools like being able to map in GeoFRED or being able to pull in data in the Excel add-in. And these things can be found here. We also have mobile apps if you want to take a look at FRED on your phone.

And, finally, we’re here to help you understand economic information and use FRED. And, so, we’ve got tutorials, FAQs and the ability to contact us and ask questions. So, if you want to know how to find data—like maybe what the data are that you’ve found or how to use any of the tools—you can always reach out to us and we’ll get back to you.

So, now, let’s maybe dive into an actual example. You can see some past searches I did. And I think I’m going to actually go ahead and start with this yield curve. So, and go ahead and search for that. You know, the yield curve data’s super popular right now. You may or may not know why, and I’ll kind of show you in a second why. But, you can see I did a basic search. I get 23 results. All the values are listed. Every search is going to work the same. You’re going to end up with these search results, and you’re going to end up with some filters here on the left. So, if you wanted to really—if you ended up with a lot of search results, you could dive in by geography, by source or by concept, and limit your search results.

I’ll go ahead and click this first one, which this is the 10-year Treasury maturity minus the 2-year Treasury maturity. I’m going to go ahead and start with that.

Now we’re on what’s called the series page. And this page is going to be the same for every single time series, everything you find in FRED. And, so, this really is the heart of that one-stop shop that I talked about. And once you understand how to use the tools here, then you really have a lot of power at your disposal and you can do a lot very easily.

Before I dive into some of the features on this page, though, let’s just talk about the data on this graph. And one of the things you can see here is that this value, the difference between the 10-year Treasury maturity and the 2-year Treasury maturity, is that the values have gone down. And I can just mouse over and kind of see them. Obviously, if the value of the 2-year Treasury maturity is higher than the 10-year Treasury maturity, this value would be negative.

Kind of looking at this graph, you can kind of see that it’s going down, but it doesn’t really tell you a whole lot. One of the things that we can do, though, is we can expand the time range. And, so, now when you look at it, you start to see a pattern emerging. And you might see that you have these gray bars. And the values are negative before the gray bars. And what’s happening here is what economists call the yield curve inverting. And this is where the long-term maturity is lower than the short-term maturity. And these gray bars are recessions. And what it appears as though is that maybe, whenever these go negative, a recession follows. And, so, this is seen as a leading indicator in the economy, and that’s why it’s followed so closely. And, so, you can look over here, as we approach zero, why this is such a popular series at the moment.

So, now, let’s kind of take a step back and look at this page as a whole. And talk about the other types of things you could do. And, again, this is going to be the same, whether you are looking at gross domestic product, the unemployment rate, homeownership rates for a particular state or county. All of this is going to be the same. So, we’ve got the title. One of the things to notice here is this code. This is what we call the series ID. That’s going to allow you—that’s a—it’s kind of like a Social Security number for this series. It’s a unique identifier. If you know what that is, you remember those, you can very quickly get back to the series.

We’ve got metadata up here at the top. So the latest value is 0.16 and you can read that as percent. It was updated yesterday. And this data is produced daily. You can, again, some other ways to change the time range on this graph. And, of course, you can always download the data, an image. You can send this straight to PowerPoint or a pdf. And it’s just going to stick everything that’s in the graph right out for you. You can also edit this graph. And, so, you can change the percents of the values. Or, I’m sorry, you can change the units of the values. You can modify the frequency. You can actually combine two series together to create a third series. So, you could maybe create your own spreads, if you’re interested, or deflate a series if you want to compare real and nominal values. You can add multiple lines to this graph.

And then, finally, you can format this. So, you can change—this is where you would change the type of graph that you’re looking at, and then also change the formatting. Things like the color, the line style, width, etc. And I’ll show you an example of that in just a second after we kind of work our way through. As I scroll down, we’ve got share links and account tools. So, again, this is where you could save a graph. You could add it to a dashboard, data list, etc. And then we’ve got notes and related content. And these are really great places to come and find more information about the series if you’ve got any kind of questions.

One thing to note about this particular series is that we’re actually calculating it from two other series. And, so, we’ve actually sourced it to ourselves to make it clear that it’s us that are doing these calculations. But if I go out to this series—let’s see. And I’ll just open that in a new tab. And, again, you can see this page is exactly the same. And I scroll down. And now you can see this one is actually coming from the H.15. And, so, I’m going to go ahead and click that. And now I’m actually right back to where we got this data. So, again, we’re an aggregator, and this is where we got the data from.

So, if you ever have any questions that can’t be answered by our notes, or maybe we’re not available to send an e-mail because you’re working late, you can quickly come here and maybe answer some questions and kind of get a jump start on your research. And, like I said, we’ve got related content. So, if there are articles about this series, or FRED blogs, or other things that have been produced by the St. Louis Fed, you’ll find links to it down here.

And then, finally, at the bottom we’ve got some other ways to maybe search for related series that are related to this.

So, let’s go ahead and scroll back to the top. And now, let’s go ahead and add a second series to this graph. Just so we can kind of compare two series and see if we can learn some more. So, I’m going to go ahead and go to my edit graph and add line. And now, if you notice, there’s kind of a pattern here to this title. We’ve got 10-year Treasury constant maturity minus 2-year Treasury constant maturity. And I’m going to go ahead and pick a shorter-term Treasury. I’m just going to go ahead and type 10-year Treasury constant maturity—and you’ll see it’s making some recommendations—minus, and now I’ll just change this to 3-month.

And now you can see I’ve got two options. And down here at the bottom, you’ll see that there’s some metadata about these. And when you’re combining, or when you’re making a graph, you really want to do an apples-to-apples comparison. And, so, as much as possible, you want all the metadata to match. And, so, you can see here this one is daily percent not seasonally adjusted, which matches our daily percent not seasonally adjusted, whereas this one’s monthly. So, we’ll go ahead and pick this daily one, and we’ll add it. And it’ll take just a second here. And now we’ve got two lines on here. The red line is our new 10-year Treasury maturity minus our 3-month Treasury maturity. And our blue line is still the 2-year Treasury maturity. So, if we look at these, you can see that the pattern tends to be the same. Although maybe the red line’s a little bit more volatile as it goes through.

But let’s go ahead and zoom in here on the end. And here you can see that actually this red line went negative. So, let’s go ahead and do another zoom in. And, so, here you can see the blue line, the 10-year minus 2-year, has remained pretty constant right around 0.15%. But that red line kind of dipped below zero or inverted. And then it kind of popped back up.

And one of the things that this shows is how similar economic data can tell two different stories. Depending on which of these indicators you’re following, you might see different things in the future. And this might be like a graph that you want to follow. Because that red line made a lot of news just a couple weeks ago when it inverted.

And, so, one of the things you could do is you could come out here each time and you could recreate this graph, which really wasn’t that bad. I mean it just took a couple seconds. Or you could save this to your account and then have it update automatically. And then just find it in your account each time and go from there.

So, let’s go ahead and save this to our account. And, so, I’m going to go to account tools, and I’m going to save this graph. And, so, the first thing it’s going to ask me for is a name. And I’ll just call this Yield Curve. And now the second part, I can set the observation range. And this is the observation range that will update automatically as new data come out from the source. So, in this case, when the H.15 updates this afternoon, there’ll be some more data that come out. And how do we want the graph to change as a result?

So, the first option is always chart the last one month of data. And this is going to set automatically, based on whatever time range you picked on your graph. In this case, I picked a month. But if you were looking at the unemployment rate, you might look at a year. If you were looking at GDP, you might look at five years. And it’s just going to be this moving window of time. 

The second option is to say, “OK, start this on March 12th and then keep adding data onto the end.” And, so, you’re going to get a longer and longer window of time. But you’re going to keep that beginning point. This is really great when you have some event that occurred on the graph that you don’t want to lose. So, it might be something like here where the yield curve inverted. Or it could be something like the Great Recession. And you want to kind of see how that is changing over time. That’s the option I would pick for this graph.

The third option, then, is to have a static observation range, which basically starts on March 12th, ends on April 12th, doesn’t add any more data to it.

So, like I said, I’ll pick this second option. And then if you end up with a lot of graphs, you can use this category structure to kind of keep track of your graphs. So, I’ll save this graph. You’ll see I get the little, “Your graph has been saved.”

And now, let’s go ahead and go back to my account. And from here I can just, say, search for yield curve. You can see it was just created a few seconds ago. Click on the link, and now I’ve got the graph here and I’m ready to do my analysis. And, just as a reminder, you know you could now—let’s say something notable happened here—you could download the data or you could download an image. You could send it directly to PowerPoint. You’re ready for your presentation, whatever kind of work you need to do. 

All right. So, let’s switch gears here and search for some geographic data. Because I think a lot of the data that you might need are going to be about, are going to apply to the geographic area that your congressperson represents. And, so, there’s a couple of different ways that you can find the data. And a couple different ways that you can use it. And, so, I think it’s worth to take a moment and show those.

The first one is we can just do a simple search. So, let’s search for the unemployment rate. And we won’t do Missouri. Let’s do Kentucky. And here you can see it. We got our search results. It identified Kentucky and unemployment rate as words that it wants to kind of tag. And again, we could filter by concept if we needed to go down. We could also go into specific types of geographies, which is really nice if you’re looking for maybe a particular MSA, or a city or a county, you know, kind of thing. Right here we’ve got the unemployment rate for Kentucky right on top. So, I’ll go ahead and click that.

And, again, we’re now on this page. And hopefully you’re starting to see a pattern where this page is always the same. Once you understand how to download data, edit the graph, kind of navigate it, it really becomes easy to use FRED.

And, so, what we could do here is—you can see that the unemployment rate for Kentucky has fallen since the Great Recession. Now, maybe you want to compare this to a different state. And say, “How is Kentucky comparing to Tennessee or West Virginia?” Maybe there’s been some policy changes in one of those states and you want to see how it impacts those states’ unemployment rate and how you guys compare.

So, one way, of course, is you can go to add line and say, “unemployment rate for Tennessee.” And here you can see we got the Nashville MSA first. But right here we’ve got the unemployment rate in Tennessee second, and we can kind of add that. And now you can see that they tend to trend together. But Tennessee has a lower unemployment rate than Kentucky.

Now, this works great for maybe one series or two series, but what if you wanted to kind of compare Tennessee to a lot of series, or, I’m sorry, Kentucky to a lot of different states. And say, “How does it compare across the United States?” Well, for that it would probably be better to use a map. And, so, on any series that has a geographic component and it can be mapped in FRED, if you scroll down to the Related Resources section, you’ll see this globe.

And, so, from here, I can go ahead and click on this globe. And this is going to take you out to another website we have called GeoFRED. And on GeoFRED, it’s going to map the data. And, so, very quickly here we can see that Kentucky’s sitting at 4.3%. And you can very quickly see how the states around it compare. And you can, of course, also zoom out and kind of see how the nation as a whole is performing. This mapping software, in addition to being able to zoom out and zoom in, it’s also fully customizable, just like our FRED graph. So, you can change what type of region you’re looking at, the types of data. You can change things about, like, whether you want seasonally adjusted, not seasonally adjusted, frequency. You can also change the colors, which I think is one of those things that we find users really have a lot fun doing.

And then, perhaps most powerfully, you can change the number of buckets. So, one of the things I can do is change this, say, to two. And then, from there, I could, say, set this value to 3.7%. So, the U.S. unemployment right now is 3.8%. So, this is going to show us everything that has, every state that has an unemployment rate lower than the U.S. national average and then everything that’s higher. And, so, very quickly I can just make a graph like this. And then you can see, these pinkish gray are below the U.S. rate and the green are above the U.S. rate. And, so, just that quickly, I can learn a little bit about how the unemployment rate is distributed across the United States.

And then, you can also download this. So, if you want to download this, you could download this as an image and put it in a PowerPoint or report, something like that. You can also get the data. And, so, if you’re really looking for all the data for the United States, across many, many dates, this is really the way to go. So, you can see we’ve got everything in here going back, I think, maybe to the ’90s. And very quickly I can just download this. And then, just that fast, I would get an Excel file that will have a matrix of all the data for every year.

All right, so, let’s go ahead now and jump back to FRED. And I’ll show you another way that you can search for data by geography. And that is to go to these categories. And, again, these top five are all at the U.S. national level. But if we scroll down to U.S. regional data, and I can click on states. And here you can see we’ve got all the states. This also has state equivalents like Washington, D.C., and Puerto Rico. It’s really up to the release—it’s really up to the source how they treat some of these geographies. So, sometimes Washington, D.C., is a state. Sometimes it’s a county. Sometimes it’s an MSA.

But, we’ll go ahead and select Arkansas. And now we’re looking at here all of the data that is at the state level for Arkansas. So we’ve got the minimum wage for Arkansas, gross domestic product for Arkansas, the unemployment rate. And there’s quite a bit of data. And you can go ahead and, again, filter by the concept, geography, source, release, etc.

But we can also go dive down. And, so, then, let’s say we want to look at data at the county level. And, so, we’ve got all the counties for Arkansas. And we can dive into one of these counties and look at the indicators. And you can see these numbers tend to be the same. That’s the number of series. They’re really close. In general, if we have a series for one county, we’re going to have it for all. There are certain cases where that won’t happen, because this data is often collected through surveys. And in counties that are really, have a very low population, sometimes that survey data can actually reveal personally identifiable information. And, so, in that case, there may not be any values for that county. But, more or less, they’re going to be pretty good coverage.

And I’ll go ahead and pick Benton County, which I believe is the home of Walmart. So, just something that’s easy for me to remember. And now I’m looking at all of the series that apply to Benton County, Arkansas. And, so, here you’re going to see a lot of macroeconomic indicators, as I talked about. So, things like unemployment rate, per capita personal income, median household income. But you’re also going to start to see some socioeconomic data that might be of value to you. So, things like percent of population below the poverty level, resident population, SNAP benefit recipients, premature death rate.

And I’m going to go ahead and click on this Equifax subprime credit population. So, here you can see this is the percent of the population that has a subprime credit score, as reported by Equifax. And this is data that we’ve gotten with the New York Fed and Equifax. And you can see that the value has—the percent has generally decreased over time. This one, even more so than with the state data, it’s like, well, there’s a lot of counties, more than 3,100 counties in the United States. How does this compare to other counties? What’s happening? What’s the story here?

And, so, this is really a case where looking at GeoFRED is incredibly important. And it’s going to take a second for this map to load, because it’s loading a lot of data, because there’s a lot of counties. But it, again, is going to zoom in. And, so, you can see here Benton County. You’ve got 26.14%. You have this little northwest corner of Arkansas, which is pretty strong. But as you kind of go around, the percentages go up. As you zoom out, this map is really going to tell a picture of the United States, which is, in general, the percentage of people in the South with subprime credit scores is higher than throughout the rest of the United States.

And, so, I think this really demonstrates how you can look at this very zoomed-in data at a very low geography, and then kind of zoom out and say, “What other kinds of stories can we tell about this?” So, in Arkansas, Benton County is really pretty performing strong on an economic sense. But when you pull out and look at the whole country, it tells a whole nother story.

So, that was my last example. Hopefully, you’ve seen how, using FRED, you can find economic data that you need, quickly visualize it, pull it from all different sources and really kind of tell a story and understand a little bit more about the economy. And, so, with that, I’ll turn it back over to David.

Benitez: So, Keith, we had a couple of questions during your presentation, which was great by the way, thank you. One of the questions was FRASER. Could you talk a little bit more about FRASER and what you can use it for? What are some of the applications? How is it related to FRED?

Taylor: Sure. So, FRASER is a digital repository. So, what they’ve done is digitized historic documents of, like, economic importance. A lot of them are related to the Federal Reserve System. And then they’ve made them all available online. So, you’ve got things like press releases and data releases. And that was really where it started. We have a product in FRED called ALFRED, which allows you to see vintages of the time series. So, if you want to see how something like gross domestic product will revise throughout the year. There’ll be multiple values for, say, the first quarter. And FRASER was essential in kind of capturing that information and backfilling it. Since then, they’ve really expanded with letters, policy statements, all kinds of extra stuff. And, so, it’s a really rich trove for understanding economic history.

Benitez: OK. So, actually, while you were answering that, another question came in. You were talking about older data series. What’s the oldest data series that we have?

Taylor: The oldest data series we have, and maybe it’s just best to show it here.

Benitez: Perfect.

Taylor: Is the population data for the U.K. And, so, here. Let’s see. Actually, so I can search for population in the United Kingdom, and I’ll get this series. Let’s see. And, as you can see, this goes back to 1700. And it’s coming from a millennium of macroeconomic data. So, they’ve got quite a bit of data here.

Benitez: My guess is that it’s probably not as secure as the more recent data.

Taylor: Yeah, yeah. Exactly. So, what—this was done by an economist at the Bank of England. And he’s going through many historical records and kind of splicing everything together to estimate a population throughout that whole time frame.

Benitez: So, on the same page, what would be the most, in your opinion, the most bizarre piece of data?

Taylor: I think my favorite is something that is from what we call the NBER Macrohistory data set. It’s the National Bureau of Economic Research. And they have this data set which basically pulls data from research projects that were done by economists at the NBER. And, so, there’s these little snippets of data. And you can see it’s across a whole slew of different categories. One of the things was for new orders of toilets. Oop, sorry, they call them commodes. Where’s that? Well, and then there’s also new orders for sinks. So, you can actually look at the—and this is from basically 1917 to 1930s. This is when the U.S. was moving to indoor plumbing. And, so, you can see if you graph also new orders for commodes, you’ll see that there’s almost a one-to-one relationship. So, every time they bought a sink, they also bought a toilet, which, I think, makes you feel good about American society and sanitation, public sanitation.

Benitez: So, somebody asked what’s the—and you’ve probably already covered this—but what is the number one data series? And I know that we have a section on FRED that shows the most popular. But, over time I’m sure that there’s always the number one data series that economists, or anybody working in the data field, always go to and always use. Or reference even in media or anything else. What’s the number one?

Taylor: So, right now, it’s that yield curve data. That 10-year, 2-year maturity spread. And that’s been popular for about a year-and-a-half now as it’s approached zero. Historically, it’s almost always the consumer price index, though. And, for those of you who don’t know, I can kind of scroll down here. The consumer price index is really a common measure of inflation. So, how prices are changing in our society. And, so, from here, I can get the CPI. It’s kind of weird. It’s measured as an index, which I think for a lot of people doesn’t make sense. But I can edit this graph, and I can just say percent change from a year ago. And now I’m looking at inflation over time. And I can, again, kind of zoom in and see that we’re right around 1.9% inflation.

Benitez: So, and again, just as a reminder, click on Chat and send me any of your questions. But we had another question come in, real quick. What is some of the new data being added? So, again, on the front page, I think you had mentioned that there was some house purchase data that had just come in. But is there anything else on the horizon that’s coming that people should be made aware or any other things that you would think, “Oh, you might not know about this, so we’d like to highlight it?”

Taylor: So, right now, we just added the homeownership rates by racial breakdown. We’ve also added state-level labor force participation rate data. This year we’re really committed to doing a couple of things. Adding additional data for releases that we already have that maybe we don’t have everything. So, there’s one, the Flow of Funds, or the U.S. Financial Accounts I think is what it’s called now, we’ve got a subset of that data. In the next, maybe, month or two we’ll add all of it. So everything will be there. We’re also really looking at socioeconomic data to add at the geographic, as geographically low down as possible. So, hopefully at the county level, definitely state, MSA, national level. So, we’re looking at trying to find some trade data from Census to add. Maybe some data on immigration. Data on—like from the USDA about the commodity, like agricultural prices, things like that. So, that’s really where we’re looking for this year. And we’ll be steadily adding that data throughout the year.

Benitez: That’s great. Well, Keith, we have no more questions, so thank you for your presentation. All that great information. And then, finally, we have our Connect With Us slide. So, take a minute to visit our webpage at stlouisfed.org. It has a number of resources, including FRED. And follow us on various social media. And feel free to come visit us here in St. Louis. Thank you for spending time with us this afternoon and we hope you have an excellent day. Bye-bye.