Meet FRED (Federal Reserve Economic Data)

Tim Lloyd: Good morning, everybody. I think we're going to go ahead and get started. So, if you want to go ahead and take your seats, we'll get started with our presentation. Give you a moment to get settled.

Well, welcome everyone to the Federal Reserve Bank of St. Louis for this morning's Dialogue with the Fed. I'm Tim Lloyd from the Communications and Engagement Division here at the St. Louis Fed and we're so glad that you can join us for this presentation on FRED.

So, if you brought your laptop or your tablet today, we've got some Wi-fi instructions at the front of the room. I'd recommend that you do connect to Wi-fi because Diego is going to go through a fantastic presentation, and you can follow along on your laptop or tablet.

I just want to go through a couple of housekeeping items before we get to our presentation from Diego Mendez-Carbajo.

If you, in the coming days you're going to receive a link to a survey after this presentation. Please take a moment to fill that out. We really take your comments seriously. That helps us improve our programming for this series.

If you need any kind of hearing assistance, we do have some tools here at the front of the room. You can go ahead and step to the front. We can help with that during our Q&A portion of the presentation.

If you're in person, go ahead and raise your hand, and I'll call on you in front of you are little microphones that are red right now, when you're asking a question, just tap that it'll turn green, and that'll turn on your mic so everybody in the room and online can hear what your question is, and for those of you in our virtual audience, go ahead and use the Q&A function in Zoom when submitting your question.

OK, now I want to go ahead and introduce you to our speaker for this morning. Diego Mendez-Carbajo is a senior economic education specialist and FRED economics champion in the research division at the St. Louis Fed.

Diego joined the bank in 2019, and I've had the privilege of joining Diego for a number of training sessions around the country. And let me tell you, you're in for a real treat. He is a true subject matter expert on FRED.

Prior to his current role here at the Bank, Diego was a college professor and received his Ph.D. in economics from Florida International University. His research on teaching and learning with data has been published in leading field journals, and he also contributes to the FRED Blog. Anybody read the FRED Blog in here?

Yes, I see a show of hands. Fantastic. Well, without any further ado, Diego, you want to take it away?

Diego Mendez-Carbajo: Thank you. You got it! There we are! We're live alright! Let me have the advance.

Well, thank you Tim for the introduction, thank you everybody for joining us this early, this very cool late day in [August].

I'm here to introduce you to the best-kept secret in the Federal Reserve Bank of St. Louis, and if you haven't heard about FRED today, you will walk away with your new favorite data discovery tool. If you already know about FRED, you want to walk away with some new insights and new life hacks to work with data from FRED.

So, as Tim introduced me, my work here at the Fed is an overlap between economic education writ large. Economic education in high schools, in colleges, but also professional economic education for journalists, for fiscal analysts, for researchers, anybody who is looking for data to make good, informed decisions.

Now, everything that I say today, inspiring and moving as it may be, will be exclusively my opinions—not of my colleagues at the Bank, at the Federal Open Market Committee or throughout the System.

Let's take it from the top. Because when Tim asked you for a show of hands about the FRED Blog, I could do the same thing for a show of hands about FRED, but my experience has been that in every room that we talk about, FRED, there's about a third of the audience who has never heard of it, a third who has heard of it, and a third who use it very frequently. So let's level the playing field. Let's take it from the top. So what is this, FRED thing that there's a logo for? We have swag. What is this thing? Why does it sound like a person? Is FRED a person? No.

What Is FRED and What Does FRED Stand for?

FRED is an acronym. There are those four letters stand for Federal Reserve Economic Data. So we are very lucky that it's an acronym that maps to a human person's name, because that makes it very approachable. Have you met FRED? FRED is your friend. Where can FRED take you? It's much easier to do these than with other databases that have very abstract names, like IPUMS, or you know, or Bloomberg? Well, Bloomberg is the name of a person, so that clearly makes it much easier to remember, and you associate the database with a person the company with a person.

History of FRED

FRED is a product of the Federal Reserve Bank of St. Louis. It's a database that is managed and curated by the research division of the Federal Reserve Bank of St. Louis. I like to welcome people when we talk about FRED in the Bank, I like to welcome them to the house of FRED because this organization is the one that put FRED together, and we like to trace the origins of FRED back to our director of research between 1958 and 1971, Homer Jones. This gentleman—it's a product of his time. I'll skip the St. Louis Fed historical academic connection between Homer Jones and the whole issue of monetarism and the connection between inflation and the monetary base. That's super interesting but a topic for another day.

Our director of research between 1958 and ‘71 strongly believed in the value of people having access to good data to make informed decisions about economics. So to that end he directed his staff in 1961 to collect data on three monetary aggregates. You see the titles there. And type data for those three aggregates, make 11 copies of those tables, and mail them to the directors of research of other Federal Reserve Banks. There are 12 in the System. So his staff made 11 copies, as far as we know, and mailed them throughout the country, and they mailed literally this: 19 pages of this, your good old analog typewriter numbers in columns, and send them out to directors of research across the country.

I want to draw your attention to this part of the memo that said, and we like it, we have the memo framed outside of the research division. It said, “I should be glad to hear from anyone who thinks that such time series have value.”

Well, yes, those series had value. The exercise of sharing information, economic data for purposes of decision-making had tremendous value. This memo soon became a newsletter, then became a news release.

It was, besides being mailed, it was also distributed by phone.

So let me just go back to this for a second. So when I'm saying distributing data by phone, so you didn't have to wait for these tables to land in your literal mail, in your mailbox—not in your inbox, in your mailbox. You could call the Federal Reserve Bank of St. Louis, the Research Division, the Thursday that the tables were updated and some research staff will kindly pick up the phone and read the numbers to you.

This became so popular and such a time sink that soon the research staff decided to purchase an answering machine to record the numbers and just play the numbers on a dedicated phone number when someone will call for that information. That's pretty smart. It's a way of doing things at a scale, making it available for the largest possible number of people.

So from there you can imagine electronic bulletin boards, the Internet, FRED was launched as a website in 1991. It hit the World Wide Web in 1995. It started as something as a link on a Netscape browser where you could download txt files with the numbers that you saw on those tables. Started with three series by mail.

By the time FRED hit the web there were 30 series. Then there were 300, 3,000. It was so popular that the stats kept on adding more data. The website became more sophisticated, you can see it has more functionality, more information, more details. We call the details about the data “metadata.” So data about the data. You can see what the source of the numbers are, the seasonal adjustment, the units, some notes about the methodology, and a link to the source.

So hold on to that notion of the distinction between FRED and data source. Because we're going to go back to that very soon. By 2002, you start seeing data visualizations, not just the numbers on the files with the numbers but graphs of the data. This is about the time 2002 is about the time that I was by then I was out of graduate school. But that's when I first met FRED. That was one of the earliest free data sources on the Internet.

More features were added you can see around 2006, you start seeing those shaded areas in a FRED graph. So besides simply plotting the data, we start adding some context for the users to interpret the ups and downs in the data. So those shaded areas represent economic recessions as dated by the National Bureau of Economic Research in Cambridge, Massachusetts.

You will see that some data series like unemployment or economic activity or consumption will have movements up or down, depending on the phase of the business cycle. We keep on adding more features over time, more functionalities, and I want to draw your attention to this version of the site over here that has a green button over there that says view map. One of the features of modern FRED, as we know it, is the ability to create maps of data. So we're going to talk about those in a moment.

The Latest Version of FRED in 2025

The latest version of the FRED site is this one. We launched it earlier in 2025. It has a clear design that improves accessibility. It has a lot of work under the hood. So you have a screen reader, you can navigate through all the pieces of the website, and it will read them aloud to you. And we reorganized the content of information on the site.

So remember, I made a distinction between FRED as a product of the research division I was talking about the sources of data. FRED has always been a data aggregator. The Federal Reserve Bank of St. Louis does not go out interviewing households about their employment status, calculating the unemployment rate, and reporting that through the FRED website. That's the work of the Bureau of Labor Statistics. The same thing goes for overall economic activity through GDP or consumer prices.

FRED is a data aggregator in as much as we go out and pull data from sovereign sources of information. So they are organizations that by law are required to collect economic information about labor market conditions, economic activity. We also have sources of data from private organizations like Indeed.com that will share the level of activity of job postings on their website. We get data from Realtor.com, Zillow, Nasdaq, all sorts of organizations that share their data with us.

FRED, in other words, will not exist without the data sources. So for us, it's always very important to acknowledge the source of the data in every FRED graph. So if you notice on this graph, just below the graph, we immediately name the source of the data, and we make it as easy as possible for anybody who's using FRED to tell a story to credit the source.

We would like to get credit, too, for making the data available. So we said, source: BLS, via FRED. But the source is going to be the organization that is named under the graph. All right, we started with online with 30 series, 300, 3,000 ... we’re now north of 825,000 series, more than 110 private and public sources of data.

Data by Geography

All the data in FRED has a time series component. So all the data is a concept observed across time. So all data series in FRED are going to look like this FRED graph at the top, this baby blue background with the blue line that goes up and down. That's going to be your time series structure. But half of the series in FRED have a geographical component, half of the series in FRED can be associated with a geography. It can be a country or a nation equivalent. It can be a state; it can be a census region. It can be a metropolitan statistical area or a county. There are nine different geographies ranging from nations to U.S. counties— that's as small and as big as the geographies in FRED get.

FRED Excel Add-In and API

How successful has this been? Who is using FRED? We're going to see the website today in our time together today. But there are multiple ways of accessing FRED depending on your needs. So if you are an analyst, and you need to have particular metrics calculated out of data available in FRED: you can use an Excel add-in and pull data straight into your spreadsheets.

If you use advanced statistical analysis software, you can use the API keys to pull data straight into your statistical analysis software and avoid having to download standalone files for each series and then importing them into your software.

I will just say, the API keys generate large, very large amounts of traffic for the website. So there are a lot of organizations and individuals who rely on the data from FRED to run their models and analyze economic trends.

FRED in the Classroom

If you're taking an economics class over the last 20 years, I would put money on the table that you've seen FRED at some point in the classroom. Instructors use FRED very easily, very frequently, to close the gap between what's in the textbook that was published maybe a year ago, two years ago that has data that is two or three years old.

Instructors use FRED to close the gap between those textbooks and what's in the news. So, there is nothing more powerful when you're talking about – hey, take it from me: I was a college professor – there is nothing more powerful than when you're talking about inflation, unemployment, economic growth to bring the students up to speed and say: “Okay, this is what the textbook says. Let's see where the economy is right now,” and pulling up FRED on the on the computer screen.

Who Uses FRED?

A third of the users from FRED come from outside of the U.S., which is an important source of traffic to and overall. We last year, in 2024, we recorded 34.4 million website sessions from approximately 18 million website users. So we're one of the largest sources of web traffic for the Federal Reserve, and we are particularly proud of these other statistics that you see there about a third of the users who land on a FRED site, a FRED Page, interact with the graph.

So you can do a search for a concept like mortgage interest rates: you might find data there, a FRED graph, and a third of the users are going to try to do something with that graph change the dates, change the units, change the frequency of the data. They're in our eyes trying to make sense of the numbers they're trying to figure out. What's the story behind the numbers? They're trying to make sense of what they see, so to us, that's very important. That gives us tell us that there's an opportunity to help people make more of the data that they find in FRED.

Meet the FRED Blog: Data Storytelling

So how do we do this? We have a whole set of resources associated with FRED, so we like to call them the friends of FRED, you know, and a very nice alliterative use of the F sound. Alright. So let me point a couple of those to you.

One: the one that team already mentioned is the FRED Blog. It comes out Mondays and Thursdays. It started in March 2014, so we have more than a thousand posts. And what we do is we look for interesting, topical, timely, relevant, engaging graphs series data series in FRED. And we tell a story based on a FRED graph or a FRED map.

Here's a couple of examples where we feature. We discuss new additions to FRED data like the Home Purchase Sentiment Index. Or we use the map functionality of the FRED site to discuss state and metro employment for the first quarter of 2025, so I strongly encourage you to keep an eye on the FRED Blog. We try to tell those stories in an engaging and compact way.

So this is not, the FRED blog is not written for [an] expert audience. We're expecting people to be curious about economics, but not necessarily familiar with all the nitty, bitty, gritty detail of the data series.

Digital Badges: Gain Credentials for Data

So the other thing that we have to make FRED accessible to the largest possible number of people, to be able to do this, to do this at a scale when it's convenient to users, is this set of microcredentials, a set of digital badges that anybody can enroll in there are free to complete, free to earn that break the overall set of skills required to work with data into seven categories. These seven skills have been identified by library science professionals as the skills needed by people working in business and economics to be proficient in the use of data. So we created standalone modules where you have an opportunity to test your knowledge on the topic, practice the topic in FRED.

And last, we give you an opportunity to transfer those skills into a different known FRED realm. So you can walk away from these microcredentials with proof that you know your stuff, so you can communicate to your employer or potential employer or your professional network that now you have, you have acquired, or you already had these skills again. So for those, that third of you who are already super proficient and a big fan, you can fly through these badges and be able to brag about them, like, I knew this all along. Those two-thirds who… you can pick up skills related to working with FRED data in your own time.

The FRED Team

This magic happens through the work. The devoted work of a group of professionals, a wide group of professionals. It is a professional privilege to stand here in front of you and talk about FRED when it comes to the work done by many people here. From that table in that picture, I can see data architects. I can see web developers, database engineers. I see two librarians, project managers, because sometimes it's about keeping people— frequently it's about keeping people on task. And I see my manager. They are the manager of the data desk. Hi, sit in the back.

You might have seen FRED in the news. We got a very warm piece from the New York Times talking about how FRED is widely appreciated in the field of economics, and one of the things that caught a lot of people's eyes is the use of photographs in the article about swag with the FRED logo. You are here at the Bank. If you want to stop by the I mean. I cannot believe that I'm saying this, but if you want to get some FRED swag, you can catch that on the way down at the Economy Museum, which should be open by the time you leave. So when you see or yeah, there we go. When you see the money cube out of the elevator. Make a left and you will go into the Economy Museum, and you can go get the FRED swag there. That was a huge hit when the article came out.

So I'm gonna stop here just to give us plenty of time to go live on the site and see what's what. When we do these demos or these trainings, as team calls them, it is most useful always to go live. And instead, just talking about the platform in a static fashion. To punch in, switch the video feed and see what's going on with FRED as of today. All right. So without further ado, I'm gonna switch and let's go live on FRED.

Tim Lloyd: All right. So before this presentation, when you were registering for the event, folks submitted questions in advance. So what I'm going to do is go ahead and start with some of those questions, and then we'll transition to in the room, and then from our virtual audience. So if that sounds good to everybody, let's get into it.

So I'm going to throw you a little bit of a softball out of the gate. This question is, what are two or three most used data sets?

Popular Datasets in FRED

Diego Mendez-Carbajo: All right. So the best way to answer that question, I would say, is just to go to the FRED site. And right under the banner you see these natural, this search box where you can type things like, you know, “GDP” or “mortgage.” You can type any words there.

And directly below we have this search, this trending search term. So our engineering team pulls this once a month, or I think it's once a week, to let the public know what is it that people are putting in that box. So if you want to take a pulse of curiosity or inquiries on economic data, that's what people are looking for.

So this is a website from the Federal Reserve Bank of St. Louis, the central bank part of the central bank of the United States. So you will find things that you will expect, like CPI, the consumer price index or PPI, the producer price index. And you can tell there is a wide variety of users in FRED, because some people look for the acronyms – CPI – so you are subject, if you know economics, you will recognize CPI as the consumer price index.

Lloyd: Sure.

Mendez-Carbajo: But you also have searches for inflation, which is the annual growth rate in the consumer price index or in the product, or in the personal consumption expenditures index. So you have people looking for concepts related to inflation with different degrees of sophistication.

You also have topics like the unemployment, or the unemployment rate, that's also part of the dual mandate for the Federal Reserve, so it makes sense that people will come to FRED looking for information on inflation and unemployment as well as more technical terms like M2, the monetary aggregate, M2. Or the SOFR, the short term, the overnight interest rate, for banking lending, and things that are more topical, like retail sales. I mean, this is something that changes from week to week.

How to Get Started in FRED

Lloyd: That's great. FRED, you're all about the users. So let's go for a couple of user questions that came in advance. And you talked a little bit about the badges. But this question, I think, is, is related to that, and maybe is more specific: “I'm not sure where to start. How do I get more training on FRED? And is it free?” I know you talked about this, but maybe we can go through again one more time and let folks know where they can get more information, how they get started.

Mendez-Carbajo: All right. So, let's take it from here. Let's say, you know, after today or, you know, live on the spot you want to click on retail sales. What is it that people are looking for? What are these concepts? So when you do a search for retail sales you're going to see there are close to 12,000 series in FRED. That's a lot of data. And if you pull the first one you’re going to see the time series of retail sales as reported by the U.S. Census Bureau. So there's a lot in this, on this page. You have day range, you have observations, you have buttons below the graph, you can edit the graph.

So this is where they how do I make more out of this. You can play around, you can click the buttons. Nothing's gonna break. So please do explore the site.

But if you want to be more thorough and explore this to the full extent of this functionality. This is where the digital badges will be if you click on “about” and go to the digital badges, that's where you're gonna see information about our asynchronous training resources where you can see the breakdown of the skills and proficiencies that will allow you to make the most out of FRED.

So what are those buttons at the bottom of the graph? They allow you to save and share graphs. There is a badge that will allow you to master those skills. You want to understand the difference between seasonal adjustment and not seasonal adjustment or data frequencies. You have that badge, that second badge there. If you want to go deeper into what are the limits of how you can act on data, how far you can make an argument based on data. We also spend time on that.

Lloyd: Yeah, I love that. And folks get a badge right? So they can share it online and brag to everyone about their FRED skills and show off online. We all like to do that.

Getting Help with FRED

Lloyd: I want to flip to the other side of the user equation and get into some of the more advanced features. You had talked a little bit about API, and some of the ways that folks can automate, how they pull data directly out of the platform and into say something, a specific tool that they're using to do some analysis.

And we got a lot of questions that were very specific about that technical process. So I was just going to wrap them up into maybe asking you: somebody's got a really technical question. Maybe they're struggling with something. Where do they go to reach out for help? Because I know that there's a lot of different ways that people can configure that. So what's the first step for them that are more advanced users that just need a little extra help with the say configuration of something.

Mendez-Carbajo: So we have over the years we have collected a massive amount of questions, requests, queries from the public, so we have made sure to create a repository of frequently asked questions, and how to guides, to do everything on the site, so how to change the units, how to change the frequency, how to change the format of the graph, how to request how to work through the Excel Add-In, and all that kind of information. So if you go to the FRED Help. As I said, I'm going to do that again at the bottom of each page. You're going to see that, FRED Help. It’s going to show you all the how-to guides for every single feature on the FRED graph.

If you're interested in creating maps out of FRED, we're going to tell you where to find them. Basically, any series that has a view map button will allow you to create a cross-sectional visualization of data, cross sections across along geographical lines. And like this, all functionalities in FRED. You mentioned the API. If you want to have access to that, you need to have a FRED account, which, of course, is free. But once you register for the FRED account, you will be able to use the API, and you can see all the technical details about the API in there.

Mapping Functionality

Lloyd: Let's go to a question we got from our online audience because you just sort of touched on this feature. But can you go into the mapping functionality? Somebody wanted to know. Can you demonstrate how to use the map mapping functionality within FRED?

Mendez-Carbajo: All right. So there are 825,000 plus series in FRED. Half of them are visible in maps. How can you find those maps? The easiest way is to name the geography that you're interested in. So, if you want to see data related to we’re in St. Louis. So let's do this. So if you just type the geography in the search box, you're going to see that there are 83,000 series related to St. Louis. Of course some of it is going to be the county. Some of it is going to be the city. The city is its own county, so it's a particularly confusing example for anybody who is not from St. Louis, but most of the audience will be here.

Lloyd: Do you mind? Just go ahead and turning your mic on, we'll go ahead and take your question. Now go ahead. It should turn green. I'll come over and help you out. I mean, I was gonna do that for you.

Audience Member #1: I'm sure it's a lot different than what's indicated by that word.

Lloyd: There you go! You're green now let's try this again. Thank you for your patience. Go for it.

Understanding Time Series

Audience Member #1: Almost anything you touch on FRED, like categories, sources ever you'll get a list, and it will say 3,000 sources. Now, who's going to look at 3,000 sources? And what do you mean by search by series?

Mendez-Carbajo: Alright. So I just started by searching here. So let me take it from the top. So St. Louis County. So I said, Let's search for geography, so St. Louis County. There we are. So when you search for a concept. FRED is going to tell you how many data series, how many individual series of observations they are. A series is something that is organized and serialized one after the other, like a TV series, an episode that follows another episode. So a data series is one observation plus another observation plus another observation.

A source is the organization that reports the data series. Alright. So in this case you have 6,700 plus series that have to do with St. Louis County. So let's see, unemployment rate. So, as I mentioned, all the series, have all the data in FRED is a time series it's a time series object. So is the data that is collected over time. It's an observation that is recurrent. And in this case is the unemployment rate in St. Louis County since 1990. When you pull data in FRED, you're going to see the whole range of observations. So we're going to give you everything that we have. So the question is, do you have data for unemployment rate in St. Louis County in the 1980? The answer will not be from this source. This source is the U.S. Bureau of Labor statistics and the data that we have from this series for this geography starts in January 1990.

All right. This type of data is available in a map. So once you see the button that says view map, you just need to click on it, and it will generate a color map of the data. The intensity of the values in the map are color: darker colors represent larger values, lighter colors represent smaller values, and the map, of course, goes from time series across time to cross section across geographies. In this case counties. There are more than 3,000 counties in the U.S. So you're going to have this very colorful, you know, wall art quality kind of visualization of data.

Sharing Graphs from FRED

Lloyd: While we're here. Do you just want to show how you can, say, download that data if somebody wanted to go deeper on that or share that graph?

Mendez-Carbajo: So let's start with the sharing thing, the share, every graph that you create, every data series that you find, every visualization that catches your eye is associated with a standalone stable URL. So a URL, is this piece out here at the top of the data browser. And when you click on that share map, you're gonna be given a couple of options to share your data visualization, you can create a map of the latest available data. So if you copy this URL, you can then paste it into an email into a text message. You can use it. You can embed it in a website. And everybody who has access to that URL will be able to go to the FRED map of the latest value of county level data unemployment in the U.S.

You can hold your applause to the end of this event. But that's pretty awesome, because you don't have to go to the BLS again and again and again to find that information. You have a URL that will give you the latest available data anytime. The other thing that you can do is if you're interested in just having a particular point in time, if you just want to show a particular distribution of unemployment in a particular year or month, you can have a fixed frozen in time value of that map. So that's one thing. So that's the sharing the maps. And if you want to see the numbers behind the maps, you can download that into a CSV file, comma separated value file, which is the plain meat and potatoes backbone of a spreadsheet, and you will have you know, let's go for it. An Excel file that will give you all those observations. 3,000 of them. I'm not gonna fire up the Excel application; will have to switch screens. But, believe me, it’s back there.

Data Sources in FRED

Lloyd: Yes, sir, you.

Audience Member #2: You mentioned the wide variety of sources that create the series. Does the Fed further vet those sources and the integrity of the underlying data series? And if the integrity of those sources happened to change for some reason, how would the Fed respond to that?

Mendez-Carbajo: So we get this question a lot: where does the data in FRED come from? How does the FRED team decide what to feature in FRED? So there's a data committee that meets regularly to evaluate different additions to FRED. So we're always looking for things that are relevant to the mission of FRED and the Federal Reserve. So we want to bring data that is related to economics that will allow people to make better decisions.

And we always source data from what we call sovereign sources, I mentioned that earlier. So the BLS, the BEA, the Census, the International Monetary Fund, the World Bank. Those are organizations that are responsible under their own parameters to produce economic data. The FRED team is not in the business of going there and tell them your methodology should be this, or your methodology should be that, or you should have—you know, we're not going to go to the IMF and said: “Hey, there is no data for this country. Get on it, and, you know, complete those data sets.” We just are the source of the trust that the public has in us is that we're going to present to them the data that is made available by the source. No cleaning, no editing, no substitutions, no cleaning the gaps. This is what the source has. This is what we make available.

Lloyd: Can I just ask one quick follow up on that, because one of the strengths I think of FRED is that you can go deep and just maybe show how somebody could find the methodology, even if they wanted to get into it. Just as an added layer of transparency.

Mendez-Carbajo: Of course. So, let's go back again to the topic of the unemployment rate. I mean, this is something that when you see the headline economic indicator, this is […] the unemployment rate reported by the BLS. So the name of the series is going to tell you something, maybe not everything that you want. So what is unemployment? What is a rate? If you take the time to scroll below the FRED graph, you're going to see some notes that describe depending on the series. Those notes are going to be more or less detailed, but for these one of the most frequently sourced, more frequently trafficked series, we describe what the employment rate is.

But you don't have to take our word for it. You can go to the source where we always link below each FRED graph. We always link to the source. And we are going to tell you, this is what the source is reporting. This is the BLS. What it’s going to tell you [is] methods for computing the data, it’s going to give you a sense of publications, news releases about the data. So FRED is going to facilitate your access to the data.

If you want to check anything about methods and distribution and the history, you can go to the source. At the click of a button.

Lloyd: We’ll go to the gentleman in the red shirt.

Audience Member #3: Thanks kind of related to. I noticed one of the badges was for using data ethically, and I was wondering if you could give us a quick summary of your view on what we should take away when we're using data to use it ethically, because I think that's important in the current climate.

Mendez-Carbajo: Oh, of course. So we start with topics, like, for example, when you're using data collected from human subjects. We introduce the user to the concept of Institutional Review Board. So you need to respect the rights to privacy of the individuals, not distributing personally identifiable information. We also talk about the ethics of crediting the sources, so giving credit where credit is due. So how to provide a complete data citation that will allow the individual to replicate your work or reproduce your work. So these are the general guidelines of ethical uses of data.

Lloyd: I know there were a bunch of hands that went up. I'm gonna go to the you—

Data Vintages in ALFRED

Audience Member #4: Thank you for this. I was going to ask. Is there a way to search by like a demographic group. So could you put like, for example, mothers’ unemployment if you wanted to. And then I saw FRED. But then I saw ALFRED, so I wanted to see if you could talk about the two.

Mendez-Carbajo: Yes, so I never tried to type mothers in FRED, but there's one series that has to do so. Of course the Census. So if you want to look for specific demographic characteristics, that search box would be the way to go. That depends on the source. Some sources like the BLS or the U.S. Census, will collect those demographic characteristics as they collect data about labor market conditions. So that will be the way to go.

And on the topic of ALFRED, which is one of the secret, dearly beloved gems of the FRED universe. ALFRED is the archival version of FRED. He's also, you know, Batman's, you know, Bruce Wayne's butler. But ALFRED is a collection of vintages of data in FRED. So every time the source produces a new release every time the BLS produces a new report for the unemployment rate. What we do is we store the previous vintage in ALFRED, and we just show the latest data in FRED. So when the source has an update in their methodologies, or they change some seasonal adjustment factors. You will see the difference in values between the previous version that is stored in ALFRED and the latest data that is available in FRED.

Lloyd: Let's go to another in the room. I know you've had your hand up, sir, in the blue shirt.

FRED for Everyday Use

Audience Member #5: Oh, the button. So my questions, I got really got two questions. I'm planning my vacation. I'm trying to figure out where I'm going to get the best value for my dollar. In what country can you give me that information? That's question one. Second one is, I'm thinking about retiring either to Florida or Nevada. Can you give me the average prices by county in either one of those in either one of those locations?

Mendez-Carbajo: So international vacations? I might have met my match. International prices by location, I mean, I guess you're asking about exchange rates. Well, that depends on which country you're interested in traveling to right. So, I mean, if are you interested in travel to Europe, just type USD to euro. So that will give you currency conversion, is that extreme? Let me try this other thing, exchange rate. Pay U.S. dollar to us product exchange rate. So that will be. That will give you an idea about how far your dollar will travel in Europe. So, this is not a geographical, so you will have to go to your question, you will have to go country by country. So that will give you the exchange rates.

For county level prices. Let’s see McLean County. Oh, yeah, prices. I mean, I know for sure that the BLS does not report consumer prices at the county level. Once a year we have regional price parities where you can compare average prices across states but we're not going have the county level prices in FRED, because the BLS statistic does not produce those.

Lloyd: I know that folks have had their hands up for a few times, so if you've had your hand up before, and I didn't call on you. Can you raise your hand now? I just want to make sure I get to some questions. You, sir. Oh, did I miss somebody over here? No, right there. Okay, go ahead here and then we'll go to the back.

Revisions to Data

Audience Member #6: Yeah, I'm just interested in updates to the data. Are there revision flags on, on, FRED, when there's been a revised date, or you have to go back to the source to see a revision flags on it?

Mendez-Carbajo: Let me go back to the whole thing about ALFRED. So ALFRED is where the previous vintages, whether they're being revised or not, that's what they store. FRED shows you the latest value. So if you want to see what's the previous vintage, you want to compare the current version or the latest data with the previous vintage scroll below the graph, look for the ALFRED button, and you will see you will be able to make that comparison.

Audience Member #6: And also how precise are the timestamps? If you're looking for like a time effect event, a time event, so do you have predictions versus realizations after announcements been made?

Mendez-Carbajo: So in terms of timestamps, we're going to tell you when the data was updated to FRED. So this is when it was uploaded. So we're not in the real time business, we're not in the co-location business of producing instantaneous data updates at the price of admission, it's a very good service, and we're trying to do this thing within, you know 30 min, 10 min, 5 min as fast as fast as possible. So that's a timestamp that we're gonna provide for users.

Lloyd: I think I might have neglected this side of the room so apologies for that. Gentleman in the in the tie. No, somebody else over here. Go ahead. Yeah, yes, please.

Future of FRED

Audience Member #7: Just curious if you're considering incorporating AI into your search engine?

Mendez-Carbajo: So like having like a FRED bot here and hmm! I'm trying to wrap my head around so you will ask FRED. Maybe our director of research can take that one.

Carlos Garriga: Yes, the short answer is, we're working on it. That's been in the bucket list for a number of years. We're trying to maneuver what the System allows us to use internally and externally. But yes, that's part of the plan. I mean, we have a lot of power users that, they come in there, and they know what they're looking for. But ultimately we have a lot of new users that want to take more of a guided journey, and the plan is to offer them a platform we internally call it, you know, FRED GPT, that allows you to look at the data in a more guided way and provide some of the notes as the user navigates through a lot of that data. And the ultimate goal is to make sure that there's a—you know, little gap between what the user would like to find and what the interface offers them. Right now, the gentleman pointed earlier I plot, I mean, I type something, and I find 1,000 series. Which one should I be looking at it? And that's a problem. As we scale it up, and we add more series by a factor of 10, 100, 1,000, it becomes much more difficult to navigate. So the ultimate goal is, make the search engine better and minimize the time that it takes the user to find what they're looking for, what they think they're looking for.

Mendez-Carbajo: So let me build on that. And so this is what human, given the resources that we have now and given the audience here, so how can you make the most? How can you find stories in FRED? How can you make sense of what you find? So you saw that story there about, you have these retail sales. Now it's changed. Now it's interest rate. If you type retail sales on the search engine, you get 11,000 series. That's a lot. We're going to show you things that are more relevant at the top of this list. But you will scroll a lot until you find something that catches your eye.

The other way to go about searching for information in FRED is to search through the FRED Blog and look for stories that have to do with the topic of your interest because these stories are written by humans for humans. And we'll point out interesting features or patterns in the data that has to do with the topic that you search. So eventually I will assume. I mean, actually, if you start, I mean, this is on the Internet. So if you have an up-to-date AI Chatbot, it will pick on some of this information, and you may end up getting landed on the FRED Blog through a Gen AI.

International Data

Lloyd: Yeah, I don't want to forget about our virtual audience as well. So here's a question from them: “If I'm focused on knowing the economic trends of the countries we mainly do business in, how do you set up your FRED profile structure? So it most efficiently and effectively—or so it is most efficient and effective to find the information quickly.” And I'm thinking maybe dashboards could be a help.

Mendez-Carbajo: That would be the way to go. So if you're interested in international data, rather than I mean, you can go two ways you can. I don't know. Let's see. I don't know why, I will put a spin in there. But you know, 2,600 series plus related to that country, so you can look by geography that way, or you can look by source.

So if you're looking for international data, the two big sources we have, I mean, we have multiple, so, but the two biggest ones will be the International Monetary Fund or the World Bank. So I'm going to go to the World Bank because it's not at the end. So the World Bank. We name the sources, the releases that we pull from the World Bank, and once you have a particular series that is of interest to you. And see a map, you can add that graph to your account. So once you create a free FRED account, you will be able to save your graphs to your account, and every time you access your account and pull that graph you will be able to see the latest data in that from that series, from that concept. So that would be my approach will be to collect the graphs that you're interested in, save them to your account, and every time you access your account you will see the latest data that will allow you to stay up to date with what's going on.

Lloyd: Yes, sir.

Personal Finance Education

Audience Member #8: I'm thinking of a few situations like the typical citizen has too much credit debt that we're failing at being capitalist, we're not efficient. And so children now governments are giving accounts, social surveys say many children and teenagers are not working part-time jobs and adding it, but they don't have the knowledge.

A 10-year-old child should know how to handle a bank account. They often don't. The biggest problem we have is with retirement, you know, we have. So the new deal, you know, you want society to care for a person's needs. And so people are sitting back and not knowing anything. They expect 20 years of leisurely living, knowing nothing. The average senior doesn't know as much about money as our pet dog knows when he's being fed. So we've instituted ignorance.

So why, like with all this information, I've you know, sometimes, you know, I run across someone in church, for instance, that is in a financial role in Chick-fil-A, and he did not know this existed. And so why don't you go out to all the municipalities, the city administrator, and make it known what is available here, and so like, you could have monthly classes and any municipality for how to use a savings account, and kids and families.

Mendez-Carbajo: Let me take it from there because I think you're hitting in my ears, that sounds like personal finance education. So the developing the skills to make good economic decisions is based on having access to good data. So you want to know what's what, before you take a 15-year mortgage or a 30-year mortgage. You want to see what's the difference between the different college saving plans that you have. So the best way to do the best way to inform oneself and see what data is in FRED related to personal finances—go to the FRED Blog and type things like “college savings.”

We're not going to tell you which college savings plan to open. But we're going to show you that in 529 plans, when you have prepaid plans versus market value between saving plans. There are very different levels of capitalization on those 529 plans. So you can learn about those concepts there.

We also send you to FEDS Notes, some research from the Federal Reserve to get more insights into that topic. So if you're interested in personal finance, this will be the way to go.

Audience Member #8: I've known of Econ Lowdown for a long time. I've touched on programs of videos. The videos are so delightful, you know. As with a master's, you know. I'd listened to a 5th grade video on utilities. I've learned things and I came one time I was the 3rd Ward Republican committeeman. There's 10 people in the 3rd Ward that may vote Republican, so they just appointed me. And so we came one time … the President of the Republican City Committee came to talk to Mary Suiter, who ran this, to … and we never could figure out why Econ Lowdown, the curriculum that was so good was not mandated in every school. I spoke about it to, this was when Adams was a superintendent of public schools. I said, “Well, this is available for nothing,” and he said he asked me, “Well, could you handle? Would you run an investment club?” because there was another company that they did something with finance. So why is this information the personal, like Econ Lowdown, why is it not mandated in schools the kids are knowing nothing?

Lloyd: Well, it's not really our role to say what, you know, policy.

Audience Member #8: Sort of make it mandatory.

Lloyd: Not quite our role, we're going to stay in our lane. But I will say that we just recently, if I can give a quick plug for economic education across the entire Fed system, everyone at all the different Reserve Banks, because there's different education or economic education areas within the different banks sort of came together, consolidated. And yes, there it is created, FRE: federalreserveeducation.org. That's federalreserveeducation.org. Incredible resources, I agree with you, I love the videos, so hats off to the studio team that puts those together, and the education specialists that put that together as well. So that's sort of our approach is we do make this material available for free, we're not in the business…

Audience Member #8: I'm saying so. I'm taking in Maryland Heights – City University, Wednesday next five evenings. Five weeks in the evening, so I'll learn all about the departments and the city administrator may well, I think, in the communication, did not know of this, and every municipality could be having activities to test personal finance and all kinds of things. Because citizens, I mean Maryland Heights Community Center has programs, senior programs, to get a person out of the house. That is so ridiculous. Why, why are seniors so lazy? I mean, they're not worth the dirt to bury them anymore. There's a lot of programs. You need to go out into the municipalities and say, “Here, do this.”

Lloyd: I totally appreciate the question. I do want to make sure we get to some other questions. But thank you for those comments. I appreciate that.

How Far Back in History Does FRED Data Go?

Lloyd: So I want to go to some additional questions we received online. Here's one that, I think is interesting. Can information be, or I'm sorry: how far back does data in FRED go? Because I know that some of them are, go back quite aways.

Mendez-Carbajo: Yes, I mean, I laugh because we have data from the Middle Ages, and it's like, what? How is that possible? Remember, FRED is not the source of the data. FRED is a data aggregator. If so, if there's a source out there that has been collecting data since the Middle Ages, we might have the data in FRED. So, as it happens, we have data population in England that the English have been collecting since 1086. So yes, we have data way prior to the Internet. Way prior to oh— this is a quiz question for a trivia night, the printing press, the ones the printing press invented.

I should beef up on that next time I go to trivia—with data prior to the printing press, so you can see, you know, the impact of the plague in population in England. And if you want to learn more about that, you go to the FRED Blog, search for plague. There you go, and you can hear more about it. You can learn more about labor markets and the plague. So that's as far back as some of the… that's the one that goes farthest back in time. The other sources will depend, you know, depending on when the source went into business. And when they started collecting data. You're going to have longer or shorter time spans for data.

Lloyd: One of the things I think might be worth pointing out here as well is when somebody's curious about a topic. Can you show off kind of how the FRED Blog can be a great place to start. I mean, you can obviously search within FRED for data series. But sometimes the FRED Blog is a great place to start and kind of start going down the rabbit hole, if you will.

Mendez-Carbajo: Of course. So this is the functionality there, this search, this search box is going to search for content within the FRED Blog. So this is not going to be topical, right, so if you, you know, going back to the topic of personal finance, if you type, personal finance on the FRED Blog, you can get all sorts of different things. What's the difference between—and I think there's a video, there's a short YouTube video on this one, on how savings personal savings calculated as the difference between disposable income and outlays.

Audience Member #9: inaudible

Mendez-Carbajo: This is the change in personal savings. So when income, when all this except exceed income, saving is negative, so people are saving less. That's the story in that graph. We have analysis done by our economists about excess savings during the pandemic, how savings are structured, the different types of liabilities that households have.

So that would be the way to look for topical information in FRED, using concepts that will come to mind immediately to you. If you search in FRED itself for personal finance, you're going to get the word finance in the series names, because what that search box does is, does an elastic search on the series name. So it accepts synonyms or family-related concepts and words. But that's going to land you on the, for example, the number of employees in the finance and insurance sector. Maybe not what you're looking for when it comes to personal finance.

Lloyd: All right, we've got a little time left. So, any additional questions in the room. Yes.

Audience Member #10: inaudible

Videos about FRED (Federal Reserve Economic Data) and Personal Finance

Mendez-Carbajo: The Econ Lowdown videos? So this is Federal Reserve Economic Education. Let me see if I can get out of this. So it will be on this site and that's a good question. I don't know how you will look for the videos within the site. Explore Resources it says, get started. So, we should be good at following directions: Get it started. Yes. Topic, resource type, video Q&As. They call them Video Q&As because they're videos that have questions associated with them. So you're gonna get the length. It's gonna get the intended… I'm hesitant to play the video because I don't think we, I would ask, are we going to play videos? And I'll say, no. So I'm not going to put our production team through this. But yeah, those are the videos.

Lloyd: In short, if you go to federalreserveeducation.org, it should be pretty easy to find those. That's a great resource and just again, it's a wonderful new site that was just launched a couple of months ago, actually, and has done quite well.

Mendez-Carbajo: We're going to put a plug in for videos, we also have a series of YouTube videos on FRED. All right. Yeah. Those videos, those wonderful videos from education are there. Interviews, short bits with our research economists. Concepts and the FRED videos are in the playlists. There we go. So videos about FRED.

Fun and Surprising Data in FRED

Lloyd: Let's get back to FRED if we can, because I've got a few more questions that have come through online or from our virtual audience. So here's a good one. What are some fun or surprising data series you recommend looking at in FRED?

Mendez-Carbajo: Fun and surprising? Yeah, I mean, that's a fun topic for me as an economist and as a data nerd, I find a lot of this stuff fun and surprising. So, for example. Just recently, early in August, we published a blog post on—hold on, gonna find it this way. Where is my Chilean saltpeter? Does anybody know what Chilean saltpeter is?

It’s kind of an old school name for sodium nitrate. So, Chilean saltpeter is rich in sodium nitrate, which is a key chemical for fertilizers and munitions. And this is a fun story for me. When we pulled this data, I was looking at the macro history database from the National Bureau of Economic Research, one of the sources in FRED. And I saw this data series of the price for the whole price of soda nitrate in New York.

What's this? And why, more importantly, in my eyes, like, why does this price go up so far? And then it came down, and then plateaued. There is something there. So there is a story. This is what the FRED Blog does. You tell the story behind the numbers. Do some research? What is sodium nitrate? It’s a key component to, as I mentioned, munitions and fertilizer. So what happened around between 1914 and 1918? World War I. So huge demand for sodium nitrate for the Chilean saltpeter. So you have high demand, fixed supply. You have a huge increase in price. But then, why didn't it remain elevated, or why did it come down so fast? Doing some more research, you figure out that there was limited international trade in sodium nitrate during World War I, so artificial – a manufacturer, alternative source of the resource was, you know, people came up with that solution to manufacture sodium nitrate. So there was no need to mine it anymore. So the price of the sodium nitrate collapsed to prices that even, you know, without adjusting for inflation, were below 1914.

So that's one of those, “Hey, little do you know…” stories that you know come out of the FRED Blog.

If you want to see something, and I'm just going to do this because I was thinking about this: lemonade, because this is the time of the year. Sometimes we get whimsical, and we pull data from sugar and lemons and figure out what's the cost of a glass of lemonade? So next time you feel like getting a glass of lemonade in your neighborhood. You are paying for those kids’ education because the production cost of that glass of lemonade is very low. Yes.

Lloyd: I think we're going to go. One more question over here. Yes, go ahead.

Helping Students Use FRED toward Educational Advancement and Career Exploration

Audience Member #11: Hi! My name is Chris. I'm with the St. Louis internship program boys and girls club. So I'm working with young adults, primarily high schoolers going into college. And I'm wondering how I can use this data to help them with their let's say, career exploration and narrowing down what it is that they want to do, or they think they want to do, as they either migrate towards college or the trades, and just trying to get an idea of what are these positions looking like post-graduation? Whether I'm going to the trades or going into college to narrow down that decision.

Mendez-Carbajo: So in terms of career explorations, FRED is going allow you to show your program participants information about number of jobs, number of employment by industry across the states. It’s going to show you their average earnings by industry.

So, I will take that even a step more, you know, step back from that and using FRED more for, like the personal finance education aspect of things—like thinking about what's the difference in cost of living between states? Right? So people thinking about “Oh, really, I would like to move to L.A.,” or “I would like to move to New York.” Well, you know, compare the price, the cost of living in those different geographies, and break it down between the price of buying goods and services versus the price of housing.

So you're just getting the students, the young people who are making personal finance decisions to start making those thinking along those terms. You know, the price of a soda might be higher in a city that in a rural place in a small town, but certainly housing is going to be substantially different. So getting those, those thoughts, getting those ideas in their heads.

Audience Member #11: How about, like, job outlook?

Mendez-Carbajo: So that would be the job outlook the BLS has a whole section of that, the Bureau of Labor Statistics. We're not going to be able to provide job outlook for industry by states.

Audience Member #11: Thank you.

Mendez-Carbajo: You’re welcome.

Lloyd: All right. Well, we're right at time. So I just want to thank everyone in the room and online for joining us today, and a big thank you to Diego for lending all of his expertise to everyone. So, thank you so much, Diego.

Mendez-Carbajo: Pleasure.

Thank You

Lloyd: There was a lot of information covered in this presentation today and in the Q&A segment. So, if you want to go back and review something, we will put a recording on our website after the event. So make sure to be on the lookout for that. Also, again, you're going to get an email after this event with a survey link in it. Please take a moment to fill that out. That really helps inform our programming for this event series.

And then also we'll include some links to some resources. I know that there were questions about personal finance. We'll put a link in there to the website that we were showing you earlier, federalreserveeducation.org.

Audience Member #8: Would you, sir? You might want all the forums right here where people can come. Maybe people don't know how fun this is. How many public meetings are there? There are a lot, aren't there? Right here?

Lloyd: Yes, that's part of our job is to engage the public. So go out and tell all your friends about this great experience and thank you so much. And again. Thank you, Diego, for your time. Thank you. All right, thank you.