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Active Learning for Future Data Gurus: FRED and New Lessons - Econ Lowdown Webinar Series, Episode 11

At a time when students often struggle with data interpretation skills, employers increasingly look for high school and college graduates who possess them.

To help your students build those skills, hear from two St. Louis Fed data experts in video from this two-section webinar.

In the first half of the webinar, learn how to use FRED for teaching economics. Teaching through face-to-face instruction and through using interactive online modules will be highlighted.

In the second part, learn how to use the FRED Interactive online modules hosted in You’ll find out how to access and use a set of inquiry-based lessons to help students develop the basic skills needed to interpret data, such as understanding indices, medians, and population adjustments. Each lesson includes primers on relevant mathematical and economic concepts, a lesson plan, and links to online data sources.


Below is a full transcript of this webinar. It has not been edited or reviewed for accuracy or readability.

Brad Straubinger: Hello and welcome to ECON Lowdown, our webinar today. We’ll discuss the active learning for future data gurus, FRED and new lessons. I’m Brad Straubinger from the Federal Reserve Bank of St. Louis, and I’ll be your facilitator today.

And before presenting our presenters, I’m going to run through our call logistics real quick. If you haven’t joined through the webinar yet, click the link you received after registering. For the best webinar experience, use the FAQ document, which can be found using the Materials button at the webinar player page.

Let me highlight a few important notes for you. You can listen to the audio through your PC speakers or through your phone. If you use the phone options, slides will not sync with audio unless you change your settings. You can do this by selecting the gray gear located on the upper right corner of the slide window, just above PowerPoint Presentation. It’s the little circle there. It’ll pop out and you should see an option for the phone. Click that and you can follow along on the phone. You can also expand the size of the slides. To do this, just use that maximize button right next to the gear shift in the upper right corner of the slide window located on the webinar player page. Also, if you’d like a PDF version of today’s presentation, you can access it using the Materials button.

And we will be taking questions at the end of the presentation, but you can submit them at any time during our call. If you joined us via the webinar, you can see that Ask Question button. Just use that. Type it in. We’ll get those questions to our presenters today.

I’m going to turn it over now to Kris Bertelsen in Little Rock, who will introduce our presenters.

Kris Bertelsen: Okay. Thank you very much, Brad. This is Kris Bertelsen. I’m with the Federal Reserve Bank of St. Louis here in our Little Rock branch. And I am—First of all, I’d like to take a moment to thank you for joining us here this afternoon. We’re grateful for that and we want to thank you for your time and your efforts with our students. We really appreciate that.

This afternoon we have the privilege of hearing from two of my colleagues, Diego Mendez and Dave Perkis. Diego will begin. He is a senior economic education specialist, like myself, in the St. Louis office, and has a vast knowledge in using our FRED for education in his previous teaching career. He taught 19 years as a professor of economics in a wide variety of courses, and always incorporated FRED into his teaching in one way or another. So, Diego will start and Dave Perkis will follow. Dave has taught economics and data analysis at the high school and college levels, and has a background in experimental economics, and likes to use games in class to teach economic concepts. So, we’ll be hearing from both those two gentlemen today. Diego?

Diego Mendez: Hi. Good afternoon. My name is Diego Mendez and I would like to share with you the agenda for today. The agenda for this webinar covers two different topics related to teaching with data. First we will talk about what active learning with data is and about what FRED interactive models are. And then we’ll hear about new lessons in basic data literacy with a new landing page and with an example using GeoFRED mapping.

So, what is active learning with data? Let’s take a poll. How many people in the audience teach with data?

Kris Bertelsen: All right. So, we will have a chance here for folks to vote. And you should see this on your screen. Do you teach with data? So, answer A is yes, answer B is no, and answer C is I don’t know. So, I’ll give folks here a few seconds, as we are doing our polling question. And we remind you we’ll have another one here in a little bit. So, we want you to be interactive with us. So, go ahead and vote. Do you teach with data is our question. I do see it on the screen right now. So, please vote if you’re watching the webinar via the webinar tool. Yes is the first answer, B is the no answer, and C is I don’t know.

Let’s take a look and see if we can see our results here. I’ll give folks just a few more seconds here to give us their answers. Looks like we have a good amount of folks here. I’m going to go ahead and stop this poll and show the results to our audience. And it looks like we had about 88% say “yes.” A little bit said “no” and we didn’t have anybody say “I don’t know.” So, Diego, I’ll turn it back over to you.

Diego Mendez: Oh, excellent. So, the vast majority of people on the webinar do teach with data. So, in that case, some of this might be familiar to you, but nonetheless, I want to cover some ground so we are all on the same page.

For those of you who are new to FRED, what is it? Short for Federal Reserve Economic Data, FRED is an online data base consisting of hundreds of thousands of economic data time series from scores of national, international, public and private sources. But what you see here is the web portal of FRED, created and maintained by the research department at the Federal Reserve Bank of St. Louis. FRED goes far beyond simply providing data. It combines data with a powerful mix of tools that help the user understand, interact, display and disseminate the data.

FRED began in the early 1990s as an offshoot of the long running legacy at the Federal Reserve Bank of St. Louis providing monetary data to help better understand the Feds’ policy decisions. And that long-running legacy, that goes far back. Copied here is the first page that the St. Louis Fed Director of Research, Homer Jones, sent his colleagues. I personally like to think that infant FRED was a paper-based newsletter from 1961.

If you join me in slide #9, I will show you what was in that 1961 newsletter. The newsletter was 21 pages long. Besides the first page that you just saw, there were 19 pages of typed figures, just like this.

Why so many pages with figures? Because those figures were the numerical information needed by economists to make policy regarding the money supply. The figures were to be used for actual calculations, computing growth rates, spreads and so on.

In slide #11, you can see the last page of the newsletter. A graph plotting the numerical information contained in the tables. The precursor to the FRED portal. The primordial FRED graph. This utilization of data was a powerful tool to observe trends, cycles and seasonal patterns. The graph allows the reader to tell a story based on data.

After paper-based newsletters and before the internet, the data were provided in list form on a dial-in electronic bulletin board system. The data were organized into categories containing roughly 300 data series and expanded from there. Today there are almost 590,000 data series.

I have published several articles on how to use FRED as a pedagogical resource. And I regularly participate in conferences focused on teaching and learning, where I give presentations on the topic of teaching with data.

Three years ago I spent a semester at the Federal Reserve Bank of St. Louis as a visiting scholar in the research division, where FRED lives. There I worked with a very talented group of web designers, web developers and instructional designers building a new set of instructional resources to make teaching with FRED even easier.

If you join me on slide #14, I will show you what I mean by teaching with data. Here’s an example of teaching of data. Let’s take the concept of real gross domestic product, or GDP, in per capita terms. Per capita is Latin for per person. This concept is part of the standard curriculum in introductory economics and the national standards in economic education.

In traditional pedagogy, the way this topic is most frequently covered in textbooks is by first describing two concepts: Real gross domestic product, gross domestic product measuring constant terms or in constant dollars; and population, the number of people residing in a country.

In slide #17, you can see the familiar formula we present our students with when discussing real GDP per capita. Real GDP per capita is real GDP divided by the population. With some basic algebra, we can show that when population is constant and real GDP decreases, the value of real GDP per capita decreases. Neat, isn’t it? Algebra can take our students places.

And here is the twist. What if we could present to a student the concept of real GDP per capita in a realistic and relevant context? What if we could use actual economic data to illustrate that concept? These are the driving principles behind the idea of teaching with data. This is what makes teaching with data an engaging and effective active learning strategy.

If you join my in slide #19, I will show you an actual example of teaching with FRED data. Let’s walk the talk. We start at the FRED website. First, we search for real gross domestic product. There are many data series in FRED related to real gross domestic product, but only one in the particular format we want. The units are billions of chained 2012 dollars, the frequency is quarterly, and the data are seasonally adjusted annual rates. Okay. So, after we select the series, we click on Add to Graph.

As you can see in slide #21, FRED plots the series in a graph. This series is the real GDP in constant dollars with a 2012 base year. Notice the upward slope of the graph. The value of GDP grows over time, except during recessions. The recessions are visible as shaded areas in the FRED graph.

Next, we will edit this graph by clicking on the date range boxes above the graph.

In slide #23, we have zoomed into the graph by changing the start and end dates of the data. We enter January 1, 2006 as the start date and January 1, 2015 as the end date to highlight the impact of the 2007-2009 recession on real GDP. The 2007-2009 recession is sometimes called the Great Recession. And you can see in the graph how real GDP contracted during that time period.

Next, we will edit the graph of real GDP between 2006 and 2015 by clicking on the orange button above the graph.

In slide #25, we can see the options available for editing a FRED graph. I will transform the data displayed in line 1 in the graph. I will transform the data by customizing the data. What does that mean? If you join me in slide #27, I will show you.

To customize the real GDP data, first I will search for the series of total population all ages. After I find the series, I add them to the graph. Note that I’m not adding the population series to the FRED graph as a new line. I am adding the population series to line 1 in the existing FRED graph.

In slide #29, you can see what happens when I customize the real GDP series by adding the population series. Now, under line 1, I have two different series: Real gross domestic product (series A) and total population (series B).

The next step is to create a custom formula that combines series A and series B. In slide #31, you can see that formula. I am writing the formula that divides real GDP (series A) by total population (series B). I multiply the ratio by 1,000,000 because real GDP is measured in billions of dollars and population is measured in thousands of people. Units matter.

Last step, I click on Apply so that the graph updates with the formula I wrote. So, now the graph in slide #33 shows the real GDP per capita between 2006 and 2015. After building this graph, we can see that real GDP per capita fell during the 2007-2009 recession and rose afterward. In fact, real GDP per capita peaked at $52,000 in the third quarter of 2007 and only reached and exceeded that value again after the second quarter of 2013. Very neat, isn’t it?

By now, you might be wondering, how can I add data assignments to my courses? The answer is in slide #36. You can add data assignments to your courses by using FRED interaction lessons from If you don’t have an instructor account already, go to ECON Lowdown and register. An instructor account gives you access to more than 300 free online teaching resources.

In slide #37, I am filtering the ECON Lowdown resources to show you the FRED interactive lessons. Currently, there are six lessons.

In slide #38, you can see the six FRED interactive lessons. They cover the following topics: 7 Activities in 7 Minutes, A Tool of FRED graphs; Comparative Advantage; Information Literacy; Nominal and Real Wages; Real GDP per Capita; and The Great Recession.

How do the FRED interactives look on the inside? You can preview each FRED interactive.

Join me in slide #40. Let me show you how to navigate a FRED interactive.

As you can see in slide #41 in a FRED interactive lesson, the top section of the screen allows you to interact with the real FRED website. The bottom section of the screen is the work area where you can read instructions, answers questions and receive feedback. Each section of the lesson is numbered and contains the specific tasks to complete: Answer a pretest, build a FRED graph, read a FRED graph, and answer a posttest.

Each section has a specific task to complete. The step by step instructions are provided for each task. For example, how to build a graph of real GDP per capita. If you miss a step, help windows pop up and guide you to correctly complete each task. When you complete a task, you much select a confidence level before checking if your work is correct. This is a marker of metacognition that will help us learn how difficult each task is.

Take the FRED interactive lessons for a spin today. Log in to ECON Lowdown and bring data into your classroom. And now I think we have a polling question.

Kris Bertelsen: We do. Thank you, Diego, great job. And I’m going to go ahead and pose this question to our audience. And thank you for your participation on the first one. So, hopefully everyone is seeing that poll on their screen right now. And the question reads what type of data do you use? A numerical, B is textual, C is both, and D is I don’t know. So, I’ll give folks here just a few more seconds. Should be seeing that question on the screen. And we appreciate everybody being interactive here today.

I’m going to take a look and see if we’re getting some results here, and we are. So, I’m going to go ahead and stop this poll and show those results to our audience right now. And it looks like A numerical has a little bit of activity, both C, both numerical and textual, about 60% there. So, that is our poll.

And now I will turn it over to our second presenter, and that is David.

Dave Perkis: Hello, everybody, this is Dave Perkis. I am located, actually, in our Louisville Branch of the Federal Reserve Bank of St. Louis. And it is good to see that at least two-thirds of our audience actually uses textual data. I know in my career I mostly used numerical data until I became a researcher. And I see that that’s filtering down to the secondary schools where more and more teachers and students are taking advantage of textual information.

Today I’m going to present some active learning lessons that we have at the Federal Research Bank of St. Louis. These lessons were motivated—had two motivations bringing them to bear.

If we go to slide #49, this is what I faced in a typical university classroom when we would talk about basic data transformations with my students. In years past, if I asked students to calculate a percent change, or to calculate—tell me what the slope of a line meant, I would get some pretty ready answers. But over the years, students were struggling with many more of these basic data calculations and data interpretations that they’re going to need in the job market. So, that was the one motivation was to develop a set of lessons that would help them do some of these basic data transformations with data.

The second motivation had to do with what librarians call the ACRL frames or standards. And these have been developed by the Association of College and Research Librarians.

And what they’ve found is that they are being asked to go into, on slide #50, they’re being asked to go into classrooms much more frequently and be in charge of helping students work with data, and work with information, and compile that information, and put it into a meaningful format. And, so, they developed the set of what they call frames, which are what we know as standards. And they are authorities constructed and contextual, information creations -- a process, information has value, research as inquiry, scholarship is a conversation, and searching is strategic. And, so, these are methodologies and frames that they use to gain access to information and to research with information.

So, we took it upon ourselves to start developing a set of lessons. And, if we go to page #51, you can see the types of topics that we felt would be most useful for students and teachers to have lessons around. So, currently on our website, we’re in develop. We have a lesson on population adjustments, one on means and medians, and then one on correlation versus causation.

And in the coming year, we will have lessons on all of these topics that I’m sure you’ve run into with your students multiple times. So, things like making corrections for nominal data into real percent changes, etc.

Okay. And, so, if we go to the next slide, slide #52, this is the link for where we will be posting most of these lessons. It’s at the And this is analogous to the types of pages we already have, such as Tools for Teaching with FRED, Tools for Teaching with FRASER, where we’re going to hold a lot of our data literacy focused lessons.

If we go to slide #53, this is the new page. It just posted last week on our website. Lessons for teaching with data literacy.

And if we scroll down on that page, on the next slide, you will see a section that says data and ACRL information literacy frames. And we’re going to review a lesson from that section today. And the lesson will be on what Diego already mentioned in his talk, adjusting for population, getting per capita measures.

Now, I strongly suggest that, when you present this lesson, that you do not tell your students the title of the lesson, because part of the lesson is that they figure that out on their own; that they come to that conclusion. Okay?

So, if we go to slide #55, we have our driving question for this lesson. How do you derive meaning from data when comparing two or more groups? And we’re going to use as our backdrop for this volunteerism, the concept of volunteering. We think the younger generations are especially interested in volunteering their time and their efforts. And, so, we think that this is something that they can sink their teeth into.

The major elements of the lesson, on slide #56—In addition to working with population adjustments, as I mentioned, we’ll also look at some of the ACRL frames, specifically about the reliability of data sources. And, then, we’re also going to review a tool that we have at the Fed called GeoFRED, which is an extension of some of the data that Diego just reviewed for you on FRED.

All right. And, so, to start the lesson on slide #57, this is the first visual that you can show to your students. You could ask them for a definition of volunteering, or you can put it up there. But then I’ve included some statistics from the current population surveys of civic engagement and volunteer supplement for 2017. The links to the source are at the bottom. And, so, if you want to find even more statistics and post them, you’re welcome too. I’ve simply pulled out some of the more interesting ones that I saw.

And, so, we have, for instance, parents volunteered at rates nearly 48% higher than non-parents. Generation X, those born from ’65 to ’80, showed the highest rate of volunteerism. Veterans helped neighbors and donated to charity more than civilians. And Americans most frequently volunteered their time to religious groups.

So these are just questions to get them thinking about volunteerism, to get them thinking about some of the numbers. And you can ask them questions such as why do you think veterans help neighbors more? Or why do you think that parents volunteer more frequently than non-parents? And get the discussion going. Okay.

If we go to slide #58, this is the information literacy part of the lesson. And, so, the next four slides will take them through the data sources and then provide questions that you can ask them to elicit discussion. So, you hand this out. They can read through the handout for the sources.

And then, if we go to slide #59, we see several questions about where the data comes from. What motivation might the agency have for posting volunteer data? Did the agency generate the data on their own? If not, where did they get it from, etc.

In this case, the two agencies that we’re investigating are on the up and up. So, it’s not a point to call these two particular agencies out. But it is a point to get students asking, “Okay, how can I trust the validity of this data?” Do these come from reputable sources, or do they come from somebody blogging from their basement, etc.

If we go to slide #60, again more data questions. And I want to draw your attention to the last one on the page, which says, “Of the methods used to collect survey data from U.S. households – phone, in person, mail, e-mail, the internet – which do you think is most reliable?” A lot of our students, what we find is that, you know, they think that all polls are taken on the internet and that everybody responds by that. And they—You know, they’re interested to find out that for this particular survey it’s taken over the phone or in person, if they have a hard time getting in touch with the person over the phone.

And, so, that can basically determine the types of people that might answer the survey. You might get a very different population answering over the internet than would answer by phone or in person at their household.

And, so, this brings up two of the major survey errors, on slide #61. And, I mean, there are a lot more errors than this, but I’ve tried to get them down to kind of the two major errors. And that is that there might be inaccurate or missing information, and the sample might not represent the population that you’re trying to study.

All right. The first error typically occurs if somebody just won’t answer some questions, or if they’re embarrassed about something like their health status or their income and they fib on an answer. And, so, surveyors have to use a lot of statistical techniques to try to make corrections for those errors.

The second error has to do with, again, if you take an internet survey, you might get more young people. If you do a household survey where you visit doors, you might get more older people or adults. And, so, it really has a lot to do with the method by which you collect your survey information. Okay. And, so, that is the information literacy part of the lesson.

If we go to slide #62, this is to elicit discussion and to try to lead your students to the fact that they have to adjust populations by dividing by the population. And, so, we have this visual, and you can post it in your classroom. And this lists the number of volunteers per state for 2015. And that’s the most recent year for which we have reliable data.

And, so, we see California has the most volunteers of these five states listed, with Utah having the least. And there is even a line where you can add in your own state for your classroom. So, I put in Indiana. That’s where my kids go to school. And you basically give this to your students—And I strongly suggest putting them in groups. And they have to say in which states listed were people most like to volunteer their time. They have to develop an answer and they have to have an explanation for the reasoning for their answer. Right?

Hopefully, in those groups, they figure out that, okay, well, even though California has more volunteers, you have to adjust for the population of the state. If they don’t, there’s a whole host of leading questions to help you get them to that point. And then, once they arrive at that point, then you go through a sample calculation of how to calculate volunteerism rates.

At the bottom of the slide, on slide #63, we see that the volunteerism rate is calculated by taking the number of volunteers divided by the population and you get an 18.1% volunteer rate.

All right. And then on slide #64, we do a very similar exercise where we’ve added a column with the number of volunteer hours, and it’s the same type of question. All right. Based on the volunteer hours in that state, and these are in millions, in which of the states listed were people most likely to volunteer their time? Hopefully by this point they understand, oh, we have to consider population. And there are more leading questions.

And on slide #65, you lead them to the point where you’re calculating volunteer hours per capita by taking the volunteer hours, dividing by population and getting 24.1 hours per capita.

All right. And, so, we will get to—In a few slides we’ll get to the assessment that you can do in class to see if they’ve understood the learning.

On slide #66, what you will do is you’ll take them through a tutorial to find the actual population numbers for each of the states. And, so, you can see here—and this included in the lesson plan and as a handout for each student, we have a full tutorial of how to find GeoFRED and navigate through it, and find the population by state.

All right. If we go to the next slide, slide #67, we can see what GeoFRED looks like once you click on the link. You have a map and then you have several tools on the left of the column. And you can pull up a state map, like I have done here. Or, if you want to, you can pull up a map of the countries around the world. And you can even drill down to a map of counties in the United States, and locate county-level data.

In this example, we’ve chosen state data. And we’re going to pick as our data, in the second line there, resident population. And we want to make sure, if we go down and look at the dates, we want make sure that we do this for 2015. Our volunteer data is for 2015 and, so, obviously, we want to pick the same year for our population.

When we go forward to slide #68, if you hover over the number for any state you’ll get a little popup window there. And then if you click on details and data, it will expand and it will show you the data source for any of the data that you view on GeoFRED, as well as show you a series of the data by year. And you can even click on the related series in FRED and it’ll take you over to FRED and it’ll give you a graph similar to the ones that Diego showed you previously.

Okay. Now, if we go to slide #69. So, this is the follow-up assessment for in class. And I usually would not grade this. I would just give it to them for practice so that you can walk around the room and see how they’re doing. So, I’ve calculated, or I’ve filled in the population for three of the states, the three top ones. And it’s up to them to fill in the population using GeoFRED, and then fill in the volunteerism rate and the volunteer hours per capita using the calculations that we just reviewed.

If we go to the next slide, slide #70, this is the answer key for the teacher. And once we fill in all of the data, you can see, sure enough, that if we base our conclusions on the numbers above we might say California has the highest amount of volunteerism. And it might have the highest amount, but it definitely does not have the highest rate. Those distinctions belong to Minnesota and Utah on the bottom, where their volunteer rates are 28% for both states.

And, then, interestingly, even though they both have similar volunteerism rates, the volunteer hours per capita, Utah is the highest by far. And this is not just for the six states listed here. It’s actually for all 50 states plus Washington, D.C. at 57. And, so, you can talk about that. You know, does Utah have higher volunteerism because of, you know, they might have a high Mormon population that likes to volunteer a lot. And we saw on one of the first slides that most people volunteer through religious organizations. And you can start to make—start to tie the data together.

And then, finally, if we go to the next slide, slide #71, this is a take-home assessment. You can use it in class or use it—give it to them to take home. Where they look up some other economic indicators for these states on GeoFRED and then they fill in the table. And then they have to see if they can make any conclusions or see any trends in the data in terms of what economic indicators would point towards a state where you might have higher levels of volunteerism. And there’s, obviously, an answer key in the lesson.

So, I encourage you to go on our new page, lessons for teaching data literacy, and look up the lesson. And there’s a bunch of information you can use to implement it in your classroom right now.

And, so, finishing that up, I’m going to hand it back to Kris and let him take over.

Kris Bertelsen: Okay. Thanks, Dave. I was just going to check in here and see if we had any questions come through. Brad, did we have some questions?

Brad Straubinger: We do have a few questions and will remind folks you can click the Ask Question button right there on your screen and just type it right in, and we’ll get it to our presenters. So, we have a few here early on.

How much does it cost to register in ECON Lowdown as an instructor?

Diego Mendez: That’s a question that we get quite a bit and it’s absolutely free. There is no cost to you or to your students. So, you effectively have access to the fruits of the labor of professional economies who vet the content, and web designers who put it out on the web in engaging formats, supported by instructional designers that make sure that whatever instructional materials we put out are properly designed for our students. So, it’s a great offer. Great value at absolutely no cost.

Brad Straubinger: All right. And then, another question we had that came is, ECON Lowdown looks great. How long has this been around?

Diego Mendez: That’s also—Now this question is getting—The answer changes. The very first version of ECON Lowdown was Solarlight in 2010 with some online modules. And it’s been growing since. Today it hosts not only materials produced by the Federal Reserve Bank of St. Louis, like the FRED interactive modules, but also materials like video podcasts and reading materials produced by many of the Federal Reserve Banks in the Federal Reserve system.

Brad Straubinger: All right. So, we did have those few questions come in. We do have a few moments here for more questions, so you can submit those. But right now we do not have any. I will remind folks that there is a survey button that you can see there on your screen, as well. It’ll also pop out at the end of this session, and we’ll also send an e-mail. You only have to fill that out once, but we do like people to fill out our surveys.

I’ll check one more time our question box. And I’m not seeing any other questions coming in. So, at this point, I’m going to ask Kris if you had any final thoughts before we wrap up today’s session.

Kris Bertelsen: Okay. Thank you very much, Brad. Again, thank you everyone for joining us. We really appreciate it. I just have a couple of things to say here in closing.

The first is that our team here in St. Louis, at the St. Louis Fed, is constantly seeking feedback from teachers who use our materials, ECON Lowdown and FRED, and, so, if you have any questions or ideas about lesson plans, things of that nature, please feel free to reach out to us. We really value that feedback and do make a lot of changes to the sites based on user feedback. So, that’s the first thing.

The other thing I’d like to mention is another webinar coming up on November 13th. This is our EConnections webinar. New semester. New secondary materials. And we’ll be highlighting resources from the Federal Reserve Banks of Atlanta, Kansas City and St. Louis. This webinar will showcase new resources for secondary teachers of economics, personal finance, general social studies, history and geography. So, we have a number of new lessons from each of those banks that we’ll be sharing on November 13th, 4:15 Central Time. And if you’d like to register for that, you can go to our website and click on events under the Economic Education tab, and you’ll be able to register for that.

And so, again, thank you for everything you do for students and for joining us here today. Brad?

Brad Straubinger: Thank you guys, again, for your expertise. And, again, that survey should be on your screen. And, once again, you’ll see an e-mail that comes your way, as well. So, you only need to fill it out once. So, just take a couple minutes, please, to do that. It helps us with these sessions.

This will conclude today’s session of ECON Lowdown. We hope you enjoy the rest of your day.

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Audience:   High School, College
Language:   English
Subjects:   Economics, Data Literacy
Resource Types:   Webinar
Concepts:   Gross Domestic Product (GDP)