Federal Reserve Economic Data

FRED recently added three series on the macroeconomic uncertainty index for the United States reported by Kyle Jurado, Sydney Ludvigson, and Serena Ng. The authors use a set of 132 individual macroeconomic time series to calculate forecasting factors and estimate period-specific measures of uncertainty. More details about their methodology are available here.

The FRED graph above shows the value of that index with three horizons: 1 month ahead (the blue line); 3 months ahead (the red line); and 1 year ahead (the green line). The relative position of the three lines in the graph shows that economic conditions are gradually more unpredictable the further we look into the future. Yet, the same lines become relatively smoother because the more-distant forecasts become  gradually less distinctive. The timing of the peaks and troughs of the data series shows uncertainty is highest during recessions, particularly after economic contractions have been underway for a few months.

The COVID-19-induced recession was an exception because its short duration, dated by the NBER, does not immediately convey its dramatic and broad scope across labor and product markets. At the time of this writing, the uncertainty index has not returned to its much lower and relatively steady pre-recession values at any of the reported time horizons.

How this graph wase created: Search FRED for and select “JLN 1-Month Ahead Macroeconomic Uncertainty.” From the “Edit Graph” panel, use the “Add Line” tab to search and select “JLN 3-Month Ahead Macroeconomic Uncertainty.” Repeat the last step to add “JLN 1-Year Ahead Macroeconomic Uncertainty.”

Suggested by Diego Mendez-Carbajo.

The FRED graph above shows two different measures of money between 2005 and 2010: The red line tracks M2, which includes cash, checking deposits, and other short-term deposits. The green line tracks the monetary base, or M0, which includes only cash and bank reserves. The ratio of M2 to M0 (blue line) is often referred to as the “money multiplier,” a measure that describes how the  supply of private money (deposits) responds to the monetary base: As banks accumulate excess reserves in their account, they expand their deposits and lending activities.

Between December 2007 and January 2009, M2 increased steadily but gradually, while M0 doubled from 837 billion to 1.7 trillion. As a result, the money multiplier dropped by half and has remained lower ever since.

Our second FRED graph separates M0 into its two components: currency (red line) and bank reserves (blue line). We see that the drastic change shown in the first graph is due to an increase in the supply of reserves following the 2008 recession. The increase in reserves is accompanied by a simultaneous change: The Fed began paying interest on excess reserves in October 2008. Had excess reserves brought no interest returns, banks would have expanded their deposit and lending activities because simply holding those reserves on the balance sheet is costly. Interest on excess reserves decreases the cost of holding them. As a result, banks are willing to hold more reserves relative to other assets, and they have less incentive to expand their balance sheet. This is likely to explain the decrease in the money multiplier. More detailed discussion about the phenomenon can be found in this article.

How these graphs were created: For the first graph, search FRED for and select “M2 (M2SL).” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Monetary Base; Total (BOGMBASE).” In the “Formula” field, type: a/1000 and select “Apply” in order to adjust the units. Next, use the “Add Line” tab again to search for and select “M2 (M2SL).” In the “Customize data” field, type “BOGMASE.” Then in the “Formula” field, type a/(b/1000) and select “Apply” to obtain the ratio of M2 to the monetary base. From the “Format” panel, under “Line 3,” set “Y-Axis position” to “Right.” Finally, adjust the time series to be from 2005-01-01 to 2010-01-01.

For the second graph, search FRED for a select “Monetary Base; Reserve Balances (BOGMBBM).” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Monetary Base; Currency in Circulation (MBCURRCIR).” Finally, adjust the time series to be from 2005-01-01 to 2010-01-01.

Suggested by Yu-Ting Chiang and Mick Dueholm.

Our FRED graphs and maps can be customized to allow you to tell the story behind the numbers. In an earlier post, we described the differences between using fractile and equal interval data legends. Today we use those customization options to create binary maps.

A binary map is a data visualization format where the range of data is sorted into two categories. The FRED map above uses that format to color the states where population increased between 2021 and 2022 (the darker areas) and where it decreased (the lighter areas). The data are reported by the US Census Bureau; in addition to conducting its decennial census, it also provides annual population counts for states and counties.

The map shows many states, including Texas, Florida, and North Carolina, that gained residents and several states, including New York, California, and Illinois, that lost residents. However, for all but three states, these population changes were unevenly distributed within the state. To show that, we can tap into the same US Census data, but at the county level.

Our second FRED graph uses the same binary format described above to identify population changes between 2021 and 2022 in each county. Notice that in every state where overall population decreased, there is at least one county where population grew. Similarly, in almost all states where overall population increased, at least one county lost residents. Only in Maine, New Hampshire, Delaware, and the District of Columbia did population increase in all counties or county-equivalent areas.*

Let’s wrap up with one reflection on the data visualizations we created. The binary maps are best suited to easily show the prevalence of increases and decreases in population across regions; they don’t allow us to visually compare the magnitude of those changes. For example, while Vermont gained 92 residents and Arizona gained slightly more than 94,000, both state areas are shaded the same color. But, if we’re interested in visually comparing data ranges using a map, FRED’s default of five fractile data intervals is a reliable starting point.

*In this dataset, the District of Columbia is a single county.

How these binary maps were created: Search FRED for “Resident Population by State” and select any of the states listed. Click the “View Map” option. Click “Edit Map” and change the units to “Change, Thousands of Persons”. Next, under “Format,” change the “Number of color groups” to 2 and “Data grouped by” to “User Defined Method.” Change the interval values to 0 for the first entry. Last, click on the colored boxes to customize the colors for each data interval.

Suggested by Patrick Wade and Diego Mendez-Carbajo.