In this session of the Econ Lowdown webinar series, presenters from the Federal Reserve Bank of St. Louis discuss strategies for using data in the classroom as an effective way to make content relevant in a variety of subjects.
• FRED (Federal Reserve Economic Data) is a powerful data source with tools that make it valuable in the classroom.
• GeoFRED harnesses the power of FRED to mapping tools so students can see the data in a geographic framework.
This webinar introduces both of these helpful tools as well as strategies for teaching and easy-to-use classroom resources.
Below is a full transcript of this webinar. It has not been edited or reviewed for accuracy or readability.
Denise Davis: Good afternoon and welcome to today's Econ Lowdown webinar. Our discussion will focus on FRED data and GeoFRED mapping for your classroom. I'm Denise Davis with the St. Louis Fed and I'll be your facilitator. I'm joined by a couple of outstanding presenters. Allow me to introduce Keith Taylor and Mark Bayles.
Now, please join me on Slide 2 and I'll cover the call logistics. If you haven't already done so yet, click on the webinar link you received after registering. This option offers a few benefits. You can watch the slides as they are advanced, you can type questions to us, download the session materials, or even choose to listen to the audio through your PC speakers.
Please note that the webinar performance is dependent upon your connection; so if at any time you're having problems, just pick up the phone and dial the toll free number. To ask questions, you can submit them at any time by clicking on the "Ask Question" button in the webinar tool.
One additional note. The views expressed in this presentation are those of the presenters and are not the official opinions of nor binding on the Federal Reserve Bank of St. Louis nor the Federal Reserve System.
Now, with that out of the way, please grab your mouse because we'd like to ask you a couple of polling questions to get this session started. So the first question you should see popping up on your screen here in just a moment and it is, Have you used Federal Reserve Economic Data or FRED in the past year? Yes or no. I'll give you just a moment to make your selection there, and I'm going to go ahead and stop that poll and show the results. It should be popping up here in just a moment. All right. It looks like 56% say yes, they've used it and 44% say no.
All right. We have one more polling question, and I'm going to go ahead and pose that. It should be popping up on your screen here in just a moment. Which best describes your experience with the St. Louis Fed's free data tools FRED and GeoFRED? A. Absolute beginner; B. Rusty, looking for a refresher; C. Intermediate user seeking new skills; or D. Power user. All right. Go ahead and make your selection. I'm going to stop that and show the results. Oh, all right. It looks like we have a good distribution. About 50% are beginners. Well, good. You're about to learn a lot today. Thanks so much for participating in our poll and allowing us to get to know a little bit more about our audience here today.
Just one more thing to share before I turn the floor over to our presenters. If you joined us through the webinar, you should see that I just pushed a link out to the FRED website. Exploration is highly encouraged. Mark and Keith will also share slides with graphs and charts through the link on each page that will take you directly to what they're referencing. Also, please be sure to download the session materials if you'd like a hard copy of the presentation as there are hyperlinks to all these websites in the PDF version as well.
Now then, with all of that out of the way, let me welcome our first presenter Mark. Take it away.
Mark Bayles: Thank you. Thanks to everybody for dialing in or linking in with us today. We're going to take an inside-out approach today in demonstrating some of the benefits of using FRED in the classroom. Rather than begin with an overview of FRED's content and data capabilities and then show you with specific examples, we're going to instead begin with a specific example and reverse the steps taken to getting the graph we started with.
If you can manage to open a browser window and follow along some of the legs, that would be great. So if this sounds a bit hard to track, don't worry. We'll run through the steps that we take a second time after we've shown you a graph so you'll be able to follow the changes. Our goal for today is for you to learn more about FRED's content and capabilities and to get motivated to bring FRED into your classroom and use it with your students.
We've got an agenda that you can take a look at. We're going to demonstrate FRED using some of the labor markets and payroll data, some unemployment data, we're going to show you about dashboards, release tables, and then we're going to get into GeoFRED and talk to you about some teaching and learning tools available on the Econ Lowdown website.
So I think our first slide is about ready to pop up here, and there it is. Take a look at this, if you would. This particular graph shows data from the BLS, Bureau of Labor Statistics, showing the change in the number and thousands of employees that were added to the nation's payrolls each month for the last year. This is a number that you've heard widely reported in the press every month.
So on the next slide that Keith's going to talk about, we'll show you the FRED settings that rendered that particular graph. You're seeing them here now, and also please take a look at that URL that you can click on.
So, Keith, do you want to tell them what they're seeing here?
Keith Taylor: Yeah, sure. So at the top left of the screen, we see a nearly identical version of the graph that we saw on the preceding page. To the right is the graph with the graph settings dropped down revealed to show that this is a bar graph with a date range from February of 2015 to February 2016 so a year's worth of data. And you can see those date selectors are above the graph on the right on the right-hand side.
All of the essential component parts of a graph are present; so we've got the title, the X and Y axis labels, scales, gridlines, legend, clearly defined plot area, data source, and the publisher which in this case is the Research Department at the Federal Reserve Bank of St. Louis. And what you're seeing here is an example of a conventional bar graph that gives us some understanding of the state of the national job situation. And so just to make this clear, each bar represents additional jobs added to the economy for that month. And the higher or taller our bar is, that's more jobs that were added for that month.
So if you look at November of 2015, for example, or October of 2015, more jobs were added in those months than say March of 2015. But, you know, this is not perhaps the most visually captivating bar chart that we could have. So what can we do? Well, we can look at a wider time range. And Mark's going to talk us through looking at ten years of data.
Mark Bayles: So the next graph you see is the same data series but the time sweep is expanded to ten years. You can do that too by clicking the "10yr" button. And I hope you're seeing that now. There you go. So it shows not only an expanded time range but it also shows a gray area which is a recession bar with job losses occurring during and after the recession.
So note that URL down in the right corner is clickable and you can open up this graph in your own web browser. So if you have the graph open in a side by side window, you can just click the "10yr" button and it will expand to the same view we have here. Also, I believe you can type in questions; so if you have questions as we go along, feel free to submit those and we'll try to respond to you.
So take a look at the top right of the graph, if you would, at the "Max" button. I wonder what would happen if we click that button. It might even give us even more insight to our national jobs market. Let's find out and click the "Max" button.
Keith Taylor: So this next graph is what you'll see when we click the "Max" button. So does it help our understanding of the dynamics of the U.S. job market? Well, I think it does. It clearly shows that there are cyclical effects on the job market and it gives us a sense of how the jobs are growing when the economy is strong or outside of a recession and how jobs decline when we're in a recession. However, this is a pretty wide graph so this may not be the best way to have any sense of granularity with the data. And so, you know, if you're thinking about graphs in terms of the three little bears, this is not the middle, cool porridge.
So let's work backwards and change this from a bar graph back to its original form as a line graph. Because it's monthly data, meaning a lot of data points over a long period of time, it won't look all that different.
Mark Bayles: All right. So now you see on the left side of the graph is the bar graph from the previous slide -- that's where we were just a moment ago -- showing the graph settings below. As you can see, we're changing it from a bar graph to a line graph. When we do that, the larger graph on the right is what displays in your web browser. So this is the same data for the same time period before but now instead of being shown as a bar graph, it's shown as a line graph.
The units, if you'd focus on the units, as before, it's the monthly change measured in thousands of persons. So now you can see in one month right after World War II -- because this graph goes all the way back to 1938 -- we lost about 2 million payroll jobs with the military and civilian employment transition. The month with the largest gain, by the way, was September of 1983 where over a million jobs were added in that particular month.
So notice, if you would, that the units on the left vertical axis has two elements here. We have both change and thousands of persons, and we're going to talk about that on the next slide.
Keith Taylor: So staying focused on the units for a bit longer, the left axis displays change and then comma thousands of persons because not only did we change the original FRED graph from a line graph to a bar graph but we also changed the units. Remember, we're working in reverse here; so let's to go the next slide and see the units of this time series before we modified it.
Mark Bayles: So, as we said, we've been working inside-out or in reverse; so here's our original starting point. This is a line graph that shows nonfarm payroll measured in thousands of persons. The graph shows the number of Americans with payroll jobs in thousands month by month. So what we mean by in thousands is if you have a hundred thousand thousand, that would be a hundred million.
So you can see today we have a bit over a hundred forty million Americans on payrolls. So instead of showing the number of jobs added or subtracted every month -- that was the changed part of the previous graph -- this graph shows the overall level of payroll employment. Think of it, if you would, as the difference of showing overall levels and changes in levels.
The next graph will show you the original graphs that FRED displays when you search FRED for payroll employment.
Keith Taylor: So now we're back to the beginning. This is the graph that FRED returns when you search for payroll employment. So if you were to come to FRED right now at this moment, search for payroll employment, you'd pull up a graph that looked very similar to this. Now we're going to retrace the steps to show how FRED makes it easy to transform data displays.
Mark Bayles: All right. So if you followed every step of that as we reversed out, that's great. If not, if you got a little confused or maybe missed a step or two, that's okay because we're going to, as we said, retrace all that.
So focus, if you would, on the graph on the left and the little red oval at the top right. The data points inside that oval are the data points that are shown in the bar graph on the right of your screen which is the graph we began with. So you can see we really zoomed in on just a little bit of the data that's available in FRED which was the first graph we saw today. So we're going to go back over the steps to show how we use FRED to get from the graph on the left to the graph on the right, the one you saw at the very beginning.
Keith Taylor: So we started by customizing the graph's units and so we changed from thousands of persons to change, thousands of persons. And so basically what this is doing is subtracting the value between two periods. So this is monthly data so you'll see the change between two months.
And from that dropdown box, you can see not only do you have the ability to change thousands of persons, but you have a number of different unit transformations you could do. So you could do a percent change, a percent change from a year ago, etc. As soon as you make that change, the graph updates; and we'll show you that on the next slide.
Mark Bayles: Before we get to that, I want to ask you to talk about transformations. So are these examples, all of those, of transforming the data?
Keith Taylor: Absolutely, Mark. These are actually changing the underlying data that we had pulled from the source.
Mark Bayles: All right. I wanted to make sure everybody understood that. So, you know, this is labeled showing changed units and this particular graph shows the effect of changing units from levels to change per month. So instead of showing the number of persons with payroll jobs -- and we said that peaked out at about a hundred forty million today -- we're now showing the change in that number every month. And that's one that you hear reported regularly as we said before.
Keith Taylor: And so here we'll make the change from a bar to a line graph, and so we start with the line graph on the right side and on the left side we show how to access the dropdown bar and select a bar graph. And so it's just really as easy as clicking that deal and selecting bar from the menu. The smaller graph is a bar graph. On the next slide you'll see the small graph and the full-size graph.
Mark Bayles: Then when we go from line to bar or bar to line, is that a data transformation?
Keith Taylor: That's a great question, Mark. Mark has a tendency to ask these really big questions. I would say no, that's not a data transformation. That's a formatting view of the data. So we're really just changing the way we're viewing the data and we're not actually changing the underlying data.
Mark Bayles: So it's just a display change and not a transformation.
Keith Taylor: Exactly, a display change.
Mark Bayles: All right. Very good. So here we have that line graph on the right side and on the left we show you how to access the dropdown and to select bar graph just to make that display change. The small graph is a bar graph, and on the next side you'll see the small graph is a full-size graph.
Keith Taylor: And so now we're going to go ahead and change the date range. And so as you recall from our first graph, we were looking at about a year's worth of data, then we looked at the year's worth of data. And so let's go ahead and change from the maximum range here to ten years by clicking the "10yr" link in the upper right-hand corner.
Mark Bayles: So here comes the graph, not with a maximum date range but rather with a ten-year date range and so we're almost back to the beginning.
So, Keith, what are some things, you know, depending just on how we focus in, whether it's one year maximum or ten years? What are some things we can see in a ten- year graph that maybe weren't apparent before?
Keith Taylor: So I think a couple things that are interesting here are, first, that you can see that employment is a lagging indicator. So a gray bar, if you recall, is a recession and in this particular case it's the Great Recession, the most recent recession we had. And the blue bar is there during the Great Recession. And coming immediately out of the Great Recession in 2009, just a little bit before 2010, you can see that those are still negative which means that the level of employment was contracting not only during the Great Recession but also after it which is what we mean by a lagging indicator, that it continues to change after the recession is over.
Then you'll see an immediate spike and then, interestingly enough, you'll see another decline in employment which, if you recall from your recent history, that was where people are talking about a possible Double-Dip Recession. As it turned out, the economists who make these determinations about whether we're in a recession or not decided that we weren't; but you can see why they were concerned about it at the time.
Mark Bayles: All right. So this is a monthly graph so we have, what, more than a quarter, four months in a row, of declines but no gray bars so we didn't have the Double-Dip Recession.
Keith Taylor: Exactly.
Mark Bayles: All right. Very Good.
Keith Taylor: And so last but not least, let's take a look at what you get when you click the "1yr" button. So the full-size graph is on the next slide and the inset graph on the bottom.
Mark Bayles: So this is what you get when you click the "1yr" button. The full-size graph is on the next slide and the inset is on the bottom, if we're on the same page here, and I think we are.
Keith Taylor: We'll do one more and than we'll show you just how easy it is to search for data you and your students want to use. So, again, throughout this, we can take questions; so if you have any questions, feel free to pose them now. We want to make sure that everyone understands that FRED is a free resource to the public and that it's unbeatable for sharing both the latest and historical data with your students.
You can use these graphs to tell stories about our economy and the world. Your students can develop their understanding of economics and increase their data fluency. FRED is truly a remarkable teaching resource. We're going to stick with the topic of jobs, but the next graph will focus on unemployment.
Mark Bayles: And, again, if you have a question, you know, you can submit those and we'd be happy to take a moment to respond if anybody wants to talk about anything.
Right now you see a graph that looks very different than what we saw before that's labeled unemployment duration. So we're keeping the focus on employment but this time instead of looking at people with jobs, we're going to display the totals for persons who are unemployed broken down by the number of weeks that they've been unemployed.
So here the Bureau of Labor Statistics is counting persons who are part of the labor force but are not currently employed. Recall that the BLS surveys thousands of households monthly and inquires about employment and available for work. You can read about how the BLS conducts that survey on their website if you want to know more.
On this graph you can see that we have a violet group and they're the ones with the longest unemployment duration, 27 weeks and over. And then we have a green group. That's the persons whose unemployment is relatively short, fewer than 5 weeks. If a person in the green area remains unemployed beyond 5 weeks, they would transition into the red 5 to 14 weeks group and after 14 weeks into the blue 15 to 26 weeks grouping before ending up in the 27 weeks and over violet grouping. The graph provides a perspective on both the quantity and the quality of unemployment during any given month.
On the next slide we'll explain how this graph was created and then, again, we're going to reverse slide by slide to our original starting point to show you how we got here.
Keith Taylor: So let's reverse the steps to see the starting point. The original graph, as we'll see in a bit, was a line graph. A couple of changes we made are reflected here. Using FRED's graph settings, we changed graph type to area and stacking was changed from none to normal.
Mark Bayles: And you can see those dropdowns. So, first, we're going to change from an area graph back to the original line graph. You see the area graph small on the left side and then our original line graph on the right. Here you can see that the graph reverts to a line style when line is selected from graph type in FRED's dropdown box, and we have four time series graphs here. Because normal stacking is still activated, the totals that you see on the graph are cumulative. And so if you keep an eye on the vertical axis, that will be explained on the next slide.
Keith Taylor: So stacking data can aid in understanding but we also don't want to mislead our students; so we're going to change stacking from normal to none, and that is displayed on the next slide. Quick before we go on to that though, the popup box you see on the left side of the graph, plot area is a tooltip. When that FRED feature is activated, hovering your mouse cursor on any point on the line will cause the values of that time period to display.
Mark Bayles: So here we're asking you to pay close attention to the scale change and we've made it pretty hard to ignore by using some red highlights; so when stacking is turned off, the quantities become absolute and are no longer accumulated. Thus, the top value on the scale drops from that high of 16,000 to a much lower 7,000. And since we're thinking in terms of thousands of thousands, those are actually millions of persons.
The next slide will show you how we added four data series to one FRED slide using the add data series function built into FRED.
Keith Taylor: So this screenshot shows you how to add more lines of data to an existing graph. And so below the graph there, there are several menus, one of which is add data series; and using this menu, you can add up to ten lines on a single graph. As anyone who's ever looked at a graph that has ten lines on it will know, you probably don't want that graph but that functionality is there if you so choose. So basically you type in the series that you're looking for, you click on that series, and the line gets added to the graph. It's just that simple.
Mark Bayles: So, as we said, it's easy to add additional series to a graph. Of course, you have to be mindful not to create confusion by plotting nonconforming units, say levels in thousands and another series that was measured in percent. It's beyond the scope of today's webinar. FRED does support labels on both vertical axes. So you can use FRED to graph disparate units without causing confusion, but we're going to stay away from that today.
FRED's search bar uses keywords to help you locate the series that you're looking for. It pays to keep in mind that Fred has hundreds of thousands of series and that some are very closely named but are different. For example, the series we added here, number of civilians unemployed for less than five weeks, this particular series is seasonally adjusted but the same data is available without those seasonal adjustments and that data also was published as an unemployment rate rather than a level or number of persons. So keep a sharp eye to maintain consistency when plotting multiple series.
Keith Taylor: It looks like we have a question so maybe we should go ahead and take a look at that.
Denise Davis: Sure. Yeah. Just a reminder, if you have questions, just click that "Ask Question" button.
So if a person new to FRED goes to FRED for the first time, how does he or she get to the line graph that you mentioned as the starting point?
Keith Taylor: So when you go to FRED for the first time, you'll be most likely on the FRED homepage and there is a search bar in the upper right-hand corner or there's a search bar right in the middle of the page. On either search bar, you would just search for either nonfarm payrolls or in this case number of civilians unemployed for 5 to 14 weeks, something along those lines, a simple keyword search, and that will work. There are also some other methods that you can use to search for a series and we'll go into those in just a moment.
Mark Bayles: I would just build on what Keith said, you know, the keyword search is very helpful. Now, some people who work with this data all the time know that every one of these 300-plus thousand series has a unique identifier code. And that first series we started on I think is the PAYEMS series, P-A-Y-E-M-S?
Keith Taylor: Exactly.
Mark Bayles: And those unemployment rates, they all have short codes as well. So if you work with these things a lot, you find yourself using those codes; but there are lots of ways to find the data.
So here we are in the how FRED did that screen where we show you the line graph with four series on the right that is changed into a stacked area graph. So we're going to take those steps to take us back to the stacked unemployment graph we started with starting now.
Keith Taylor: So series can be added, you know, one at a time until your graph is complete. So here we see a line graph of one series. So on the right-hand side, we have number of civilians unemployed for 27 weeks and over. And then the final graph with all four series is on the left. So, again, we just want to type in our keywords into the search box there. Be mindful of the particular series and the metadatas of seasonally adjusted, frequency, units, things like that, when we select the option and then click the add button and very quickly you can add the four series to create this graph. Next we'll activate the stacking.
Mark Bayles: All right. So an indicator that we're using stacking, notice that the scale change, you know, we change stacking from none where you have, you know, the vertical access peaks out at 7,000 and we turn it on to normal stacking. There's also percent stacking, but we're not going to go there right now. So with normal stacking, so now the graph's line values are summed, you know, the numbers of people who were employed, you know, fewer than 5 weeks and 5 to 15 and so on are all added together and that's why we have the scale change on the Y axis.
Keith Taylor: To get from the stacked line graph shown on the left to a stacked area graph, we change the graph type from line to area and it will automatically fill in the areas with the area chart.
Mark Bayles: So, once again, we're going to reverse the steps to see our starting point so you can see how easy it is to use FRED to create these kinds of graphs.
Keith Taylor: And so here we are back at the beginning graph shown full-size with normal stacking. And also to go out to the question of the user earlier, if you want to just pull this particular graph up on your computer, you can actually use that URL in the lower right-hand corner. And it may not be obvious to you that it's a URL but it's a short hash, something like what you might see on Twitter; and so it's myf.red/g/RRRrl.
But on all of the graphs here, we have a hash something like that which will take you out so you can see this exact graph that we're working with. And then from there, you can also modify that graph yourself so you can explore these tools and play with them as we go along. So those are a couple things I think we'd like to just point out about this graph.
And, you know, Mark, is there anything that stands out on this graph as an area chart as opposed to maybe if we saw this as a line chart or a non-stack chart?
Mark Bayles: Well, I think perhaps you're alluding to the fact it gives us insight into both the quantity and quality of unemployment that we saw during the Great Recession. That violet top tier of area really peaks up from and after 2010 before declining back down to the kind of levels. And that shows us that, you know, as we heard reported a lot during the Great Recession that not only did we have really high unemployment peaking at about 10%, but that we had a lot of people who were jobless for many, many weeks. So while the green area seems, you know, fairly consistent over time, the violet area was at all-time highs during the Great Recession.
Keith Taylor: Absolutely. So if you're new to FRED, we want to make sure that we impart a sense of the depth and breadth of the data available through FRED as well as the scope of FRED's functionality.
First of all, it's important to understand that FRED is a data aggregator that provides a common interface for dozens of providers to distribute their data. FRED never edits the original data or filters it in any way. You always have access to the original raw data and can download it to your work station or personal computer. You can create original content and save it to a free FRED account. You can also assemble collections of graphs and data like the ones shown on the right in a dashboard and return to them as a ready reference or share with students or colleagues. You can also save graphs so that they automatically incorporate updates whenever they occur, weekly, quarterly, annual, for example.
Denise Davis: Okay. So if you've been following along, we just want to check in with you and see so get your mouse ready. I'm going to ask you another polling question. It should pop up on your screen. And we just want to know, Have you opened up a FRED graph during this presentation? Yes or no. So I'm going to go ahead and give you a second and stop those results. All right. And it looks like a number of you have been following along today. That is awesome. All right. Back to the presentation.
Mark Bayles: Okay. And I would encourage you if you haven't looked at one of those graphs yet or just gone into FRED and started clicking around to go ahead and do it. It will give you more familiarity and practice.
So you see, you know, what does FRED have and there's an example of the dashboard on the right side of the screen so, you know, there's more to FRED. What else does FRED have? You have access to data in a number of major categories, several of which are shown here, things like money and banking and, you know, we've seen some of the labor market data that we have but it's also regional data, production and business, and FRED has in the last year added a lot of global development data. Things like life expectancy, fertility, and literacy data from all over the world from the World Bank. Is that our source for that?
Keith Taylor: Yeah. We get a lot of that global development data from the World Bank and a little bit from IMF and the Penn World Tables.
Mark Bayles: The two graphs you see here as examples though are domestic for the United States. The top one I believe has some labor force data. We have a blue line and then we have a green and a red line which we have the male and the female. And the green and red, they sum in that top graph to form the total labor force which is the blue line.
And then the bottom one is a percent change graph where I think we show the percentage change month by month for e-commerce as well as retail sales. So you can see where those two series kind of track each other during the recession with a downturn, but the percentage change for on-line purchases fell less than retail sales overall in the United States. So we have some, you know, very up-to-date series, things like e-commerce.
Keith Taylor: And, Mark, I really think we missed a trick here on that first graph. We should have had the male labor force be blue, the female pink, and the overall green which does highlight I think with both of these examples that if you are telling a story like that, you can format the lines, the colors of the graph, things like that, to make it more readily available for people.
Mark Bayles: So that dashed line you see on the bottom graph for e-commerce, is that automatic or is that a user choice?
Keith Taylor: That was a user choice.
Mark Bayles: Okay. So very configurable, very customizable.
Keith Taylor: So another way that you can navigate through and make some status is using release tables. And so here we're looking at the Bureau of Labor Statistics release table for unemployment duration, and this is yet another way that you can actually find data in FRED. And so on this example, we checked the boxes next to the two data series on the left-hand side and these are the shortest and longest unemployment durations measured in weeks. And then we just click add the graph and we'll get the graph on the right-hand side.
So we could have used the same technique and we could have selected all four of the lines set for the unemployment durations and very quickly it created the graph without having to use the search bar functionality we highlighted earlier. You know, and it's easy to find these release tables. If you just click the browse data bar release on the FRED landing page, you'll see an organized list of links to the dozens of release tables that are available.
Mark Bayles: I want to stick with release tables just for a second and tell you there are dozens of those. If you find one that has what you're looking for, you'll come back to it time and time again.
What do we see here, Keith?
Keith Taylor: So here is, you know, my favorite graph of the presentation and if you'll excuse the pun, it's a scatter logical graph, if you will. And what this shows is the relationship between the new orders for lavatories or, as I like to call them in 2016, toilets and the new orders for sinks. And as you would hope to see, there's a very direct correlation between when someone orders a new sink, they also order a new toilet. And especially if you look at the time range on this, if you look on the graph up at the top, you'll see that it's from April of 1917 to December of 1931 which corresponds with the time in the United States when we were moving from outdoor plumbing to indoor plumbing.
And so this is a scattered plot and you'll see that the dots represent pairs of data; so, again, in this case it's new orders in thousands for sinks on the X axis and new orders for thousands of lavatories on the Y axis. And this is taken from our NBER Macrohistory Database, and this is a very interesting data set. Economic researchers went through and pulled the small data collections and they wrote papers on them and they were published in this NBER or National Bureau of Economic Research Macrohistory Dataset.
And so there's all kinds of intriguing things that we can use to show data. And from a historical standpoint, it sort of gives concrete examples of these abstract topics. And so one last thing to note is that, you know, this graph doesn't have a time element, rather the X axis is in sinks and thousands and the Y axis is in lavatories and thousands and where the two time periods align, that's where you get a data point. So let's take a look at how this was created.
Mark Bayles: All right. So the next slide is going to show you the original graph and how we use the add data series feature to include a second time series. So we started out searching for, what, lavatories and then in our keyword search we put in kitchen sinks and when we got the hit we were looking for, we clicked add series and FRED automatically generated the two-line graph you see on the right. You can see that those two series align very closely but not perfectly, and we have highlighted some things in red to make it easier to see how we did this.
Keith Taylor: So a couple of key settings are shown on the insets on the left. Changing the graph type to scatter is all that it takes to generate a scatter plot from two data series, and each dot represents a pair of data for an identical time period. Also highlighted are the mark type and the mark width which allow you to change the shape of the dot and the size of the dot on the graph.
Mark Bayles: So, Keith, you're a data guru. We see this almost 45 degree line described by the dots on the graph on the right. What's that tell us about the relationship, if any, between new orders and, you know, this period of American history for lavatories and sinks that Americans were buying?
Keith Taylor: Well, I think it clearly shows that there's a direct positive relationship between new orders for sinks and toilets, as I like to call them in 2016.
Mark Bayles: So all employees must wash hands was the rule then as well.
Keith Taylor: Exactly.
Mark Bayles: All right.
Keith Taylor: FRED is fun. Don't let anyone tell you otherwise.
Mark Bayles: Well, and, you know, we know at least one classroom teacher who is using these two series to teach students about, you know, changes in American infrastructure and social history and how life was changing after World War I and during the 1920s as America modernized throughout the country.
So visitors to the FRED landing page, which is what we see now, are greeted by an invitation to browse data in a number of ways. You can see there you can do it through tags, categories, and so on. We showed you an example of a release table awhile ago; so we're going to show you about a newly updated series. Here you see the latest updates under FRED news and there's a FRED blog. Here we got a reference to nine new series on fed funds that are published by the New York Fed that have recently been added to FRED's database.
The bottom half of the page uses tabs starting with "At a Glance". There you'll see the latest values for FRED's most popular series. FRED uses crowd sourcing. It pays attention to what the users use and those get the most coverage starting with the most popular series which is CPI. People are very interested in inflation. And notice there's a sparkline graph there with an arrow showing the direction of the latest reported value. So if you click on any of those series there on the bottom half of the page, it will take you right to that particular FRED graph.
Keith Taylor: And so at this point you might be asking, you know, how can I learn to use FRED? What more is there for me? And we have a number of resources here at the St. Louis Fed for that, the first of which is the Econ Lowdown and that has a ton of tools for teaching with FRED, you know, including these 10 Activities in 10 Minutes, an activity on GDP, and my personal favorite which is this Lifetime Inflation Activity which basically allows you to, you know, query your student, you know, when were you born and very quickly using FRED look at inflation or the change in prices over their entire lifetime. It's really a great way to engage with them personally and kind of make these abstract concepts real. There's also help in the form of tutorials and videos and all of that can be found on Econ Lowdown.
Mark Bayles: So speaking of Econ Lowdown, we have activities that are especially keyed to FRED as Keith said, ready to download for free through our Econ Lowdown educator portal. You know, searching Econ Lowdown for FRED or GeoFRED will return activities with step-by-step instructions that you and your students can use not only to teach basic econ concepts but also to build familiarity and experience in exploring FRED and creating custom content.
So here you see on the left, this is a couple of screen caps from the 10 Activities in 10 Minutes. It shows just how easy it is to get acquainted with those kind of dropdowns and graph settings and adding series, things that Keith and I have been referring to. And it doesn't take ten minutes. I've actually seen somebody run through those ten activities in 61 seconds is the current shortest time I know of.
And on the right is another Tools for Teaching with FRED activity where the elements of GDP, you see we've got another area graph. This one has a transformation and that's a feature that we wish we had more time to talk about where students will bring out both the values for imports and exports and then subtract imports from exports and that will generate that bottom line that for the United States you can see is below zero. So, again, easy to create custom content and easy to teach these basic concepts using FRED.
Keith Taylor: And so FRED isn't just for econ teachers and students though. Here are two graphs that illustrate FRED's value in an AP Government class, and the course focuses on the same six nations each year. And so imagine being able to generate these graphs and store them and then just pull them up each year for use in your class, and that's what we've done here with the dashboard.
And so on the left we have a fertility rate data for the six countries and on the right we have life expectancy, and so both of these graphs are part of a larger dashboard which is a collection; and the data update automatically as it becomes available. If you or a colleague are interested, you can find the dashboard by searching for the AP Government.
It looks like we've got another question; so let's go ahead and take a look at that.
Denise Davis: Yeah. We do have a couple of questions that just came in. On the scatter graph where do the years go?
Mark Bayles: Oh, that's a reference I think to the fact that when we had the line graph for kitchens, for sinks and lavatories, you saw the quantities on the vertical axis and the dates, the years, on the horizontal axis. But when we convert that to a scatter graph, we're going to look at relationships between data pairs, not over time but I guess we start, what, with the lowest quantity and pair that with the matching one from the same date in the scatter plot function in FRED. Is that correct comment?
Keith Taylor: Yeah. The value for lavatories and the value for sinks on the same date will match those two data points up and graph them.
Mark Bayles: All right. So we sacrificed, you know, that time component to analyze those data pairs in a different way in a scatter plot in FRED.
Keith Taylor: Absolutely.
Denise Davis: All right. Thank you guys.
Mark Bayles: Keith, sticking with the six nations, if we could for a minute, you mentioned the auto update. I just want to make sure. Is that something that has to happen or is that a choice?
Keith Taylor: No. That is a choice. So when you create a graph, on the page there's the ability to take the URL and that will always take you back to that graph as you see it with the observation range that's set. If you create a free account and you save that graph, you'll be given the opportunity to have an automatically updating observation range if you want so you can have it update and show the last end period.
So if you were showing ten years of data, it could show the last ten years of data or you could have a fixed start point so you just keep adding observations onto the end or you can have a static observation range, something that would be appropriate maybe if you were looking at the Great Recession and you wanted to make sure you always had that in view.
Mark Bayles: Great. So we're going to talk some more about searching FRED for data. That's the slide you're seeing right now. Here again on the left is the FRED homepage and, you know, we talked about that keyword searching. Here we typed in fertility rate USA into the search window, and the right side of the window shows the results that FRED returns for that and we've highlighted those with red and put an arrow on it so you could see it.
The first hit sorted by search rank, again, you know, this is that user sourcing is what we're looking for. After that, the search algorithm starts returning hits for things like not fertility but fertilizer; so make sure that you keep a sharp eye on the results and click on the ones you want.
So if we wanted to just limit the search to fertility only, FRED supports tags or filters and if we clicked on the tag for fertility that's highlighted here, then you would only get results for the USA and fertility and there's five hits for that. I'll tell you, we don't show you that but I'll just go ahead and tell you that that would limit the universe of hits to five. So on the next slide, you're going to see what a FRED user would see when she clicks Fertility Rate, Total for the United States.
Keith Taylor: And so here's the fertility rate graph for the USA over the last 50 years of data. You know, and it's great being able to see the date for one nation as we do here or, you know, we could add, as we said, up to ten series, although that would be a little crazy, and, you know, see this as we did in the dashboard. But if we want to see, you know, the fertility rate for every country in the world, really the only way to do that, the best way to do that, is with a map and this is where GeoFRED comes in.
And so for many of our series in FRED there's a corresponding map in GeoFRED. And whenever that's the case, there is a link on the sidebar on the left-hand side of the screen that will say map in GeoFRED. And if you click that link, we'll see a map here on the next slide.
Mark Bayles: And just as we invited you to get into FRED, we invite you to get into GeoFRED. Here you see the GeoFRED links to FRED and it automatically generates colorful and informative maps for which every year's data is selected. As with all the FRED graphs, these maps are downloadable, shareable, linkable, and free to users anywhere in the world, and you can also save those to a free account and return to them very easily.
Also, here we got the most recent year. Are we limited to one year's data in GeoFRED, Keith?
Keith Taylor: So on any given map, we are limited to one year's data but we can navigate and make any year in which data's available. And so in the lower left-hand corner of the map you'll see that there's a legend, and one of the first things in there is the date label with the date. And so this is for 2013, but those little carets there or greater than/less than signs would allow you to arrow through the dates. And so if you click that left arrow, it would take you further back in time. And so I believe this had about 50 years of data so you can really navigate quite far back in time.
So let's go ahead and take a look at another example. So not only does GeoFRED have data at the international or the country level, but we also have it at the state level, at the metropolitan statistical area level or MSA. And if you're not familiar with an MSA, those are designations by the U.S. Federal Government of certain geographic areas that have common economic activity. And so generally the way to think about them is that they're the big cities in the county; so St. Louis, Chicago, New York.
And also a lot of the smaller areas also have their own MSAs, and we can also map data at the county level. And so in this example, we've got the median household income for St. Louis County. And, again, it's great to see how that's changed over time from 2004 to the present. But if you want to compare this county to a bunch of other counties maybe throughout Missouri and Illinois, the best way is to click on that map, the GeoFRED link, and take a look at the next slide.
Mark Bayles: And the next slide is just a zoomed in small section of a United States county map that displays data for all 3,000-plus county and county equivalents in the United States. So the labels and quantities that you can see zoomed in here can be switched off by the way. That's a tool, a control that's available in GeoFRED. It's really easy to turn those on and off.
And if you click on any one county shown here, it would automatically bring up a link to that county's FRED graph just as we started with St. Louis County's FRED graph for median household income. So that shows a real obvious connection between FRED and GeoFRED and it allows users to seamlessly move back and forth between the two data portals.
Keith Taylor: And so just like we have FRED resources for bringing FRED into your classroom, we also have the same resources for bringing GeoFRED into your classroom. And so we have tools for teaching with GeoFRED like creating and analyzing a binary map or mapping an oil boom. And so the second map there on the right is mapping an oil boom which really gives us an interesting view of how data can be different from the national level to the county level.
And so what that map is showing is the month in which unemployment rate was at its highest during the Great Recession which was I think about 10% and then we have the three colors that represent various levels of unemployment rates. And so during that time, the range was something around 3% north to 30% I believe. And so the yellow is 10% or higher and the dark blue is actually the lowest rate. I believe it's below 4% or 5 %. And so it really gives us the sense of what's going on in the economy.
And, of course, as you're probably looking at this, you're thinking to yourself, well, this is fracking or the oil boom that was going on in the Great Plains. But it really gives a good sense of how the unemployment rate for the whole country can be 10% but we've got areas of 30%, we've got areas at 5% and how you really have to explore the data to understand what's happening.
Mark Bayles: And GeoFRED let's you see the full picture, not just for one county one value at a time. So once you've created a GeoFRED map, it's really easy to save it or distribute it to students. Here we show it takes just one click to share the link with your students or to imbed it into a teacher's web page that's sharing a command in GeoFRED. You can also download, print, and save to a free GeoFRED account.
Also, you always have access to the raw data. Just click download and it will give you the option of downloading the raw numbers that are all present there to a spreadsheet for all 3,000-plus counties for the time periods shown on the map and all time periods for that series of data.
Keith Taylor: And so hopefully Mark and I have shown you some ways in which you can incorporate, you know, FRED and GeoFRED into your classroom, you know, especially using this reverse order approach of starting with a finished graph and working back to something that you'd actually find in FRED.
Just a few last things to highlight. If you need help with FRED or GeoFRED, you have some ideas to share, helpful beauty hints, anything, you can e-mail us at email@example.com or you can call us. And you can call and talk to a real person who will explain the date, will explain the tools, you know, and try and help you make the most of this resource. Plus, if you follow that help and information link, there's a ton of on-line resources.
And then we also have the Econ Lowdown and, you know, we want to hear from you too. And there's a subscription mailing list that will make sure that you are always getting the latest content on FRED, GeoFRED, and all things Econ Lowdown. I think it's important to stress that Econ Lowdown is not just FRED and GeoFRED content but personal finance and micro and macroeconomic topics.
Mark Bayles: As we say, from pre-K through college or cradle to gray on Econ Lowdown.
Keith Taylor: Well, thank you very much and we'd be happy to take any questions.
Denise Davis: Yeah. We do have a couple of questions, but I also want to be mindful of time. If anyone needs to drop off at this time, the presentation will be available at a later date and I'm also going to go ahead and push the survey out to you. Please complete it so we know how we're doing.
And I'll go ahead with the questions now. How can I create a dashboard for my class similar to the one you have shown?
Keith Taylor: So there's a couple ways that you can do that. The easiest way is when you click on that dashboard and you follow the link, there will actually be an option to save that dashboard to your own account. So creating a user account is a pretty straightforward process. If you don't have one, in the upper right-hand corner there's a register, give us your name, e-mail address, you'll get an account. And then once you have your account and you're logged in, you can save a dashboard to your account and then you can modify it to your heart's desire and it won't impact the publicly available emergent.
The other way that you can do it is if you have an account created, you can go into your user account and there's a dashboard link and create a dashboard and you can go through a custom create it using either save graphs or widgets of your choice.
Mark Bayles: Yeah. And real quickly, it's not just graphs. There are other widgets you can save, lists of data you can save, tables of data, there's room to write in text, there's blank spacers you can use to organize the dashboard so it will be obvious to your students where they should look and where they should go next. You can also keep that dashboard private while you're working on it until it's finished and you're ready to share it with students or colleagues, right?
Keith Taylor: Yeah. Absolutely. And if you want some example dashboards beyond what's in here and what's on Econ Lowdown, feel free to e-mail that firstname.lastname@example.org and we can send you a couple examples, again, some that you can just add straight into your own account and sort of jumpstart your creative process.
Denise Davis: All right. Thank you guys. We do have another question. Can I search from within GeoFRED or do I have to search FRED?
Mark Bayles: They both support keyword searching. You know, Keith showed you and we showed you how a lot of the FRED series just give you that just click the button map to GeoFRED so that's one way to get there. But if you're in GeoFRED, it supports searching as well.
Denise Davis: Okay. Thank you guys so much. I don't see anymore questions at this time. Again, if you joined us in the webinar tool, you likely saw a survey link pop up on your screen. We'll also be sending out the survey via e-mail and you only need to fill it out once. With that, I will officially bring this session to a close. Thank you so much for joining us and I hope you have a great rest of your day.
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