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The monetary multiplier and bank reserves

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.

Visualizing changes in population using binary FRED maps

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.

The pandemic’s impact on household spending on healthcare

Differences in the demand for goods and services

The FRED Blog has examined the impact of the COVID-19 pandemic on the overall level of economic activity and employment in the healthcare sector. Today we explore a related topic: how households changed their healthcare spending during that time.

The FRED graph above plots data reported by the US Bureau of Economic Analysis—specifically, the percent change from a year ago in the annual inflation-adjusted value of three types of household healthcare expenditures. Thanks to the graph, we can visualize the impact the pandemic had on household spending on medical products, appliances, and equipment (blue bars); hospital and nursing home services (red bars); and outpatient services (green bars).

During the outbreak of the pandemic in 2020, annual household spending on health services (both inpatient and outpatient) decreased. This was a first since 2002, when data are initially available. However, annual household spending in health goods increased. What gives? The combination of broad mandatory social distancing and strict epidemiological protocols in medical facilities can likely explain the decreased demand for face-to-face health services while demand for health goods didn’t wane.

FRED has data on household expenditures by type of health good, and we will tap into those to gain additional insights into consumer spending.

Our second FRED graph shows the breakdown of total household spending on health goods into its two subcategories: durable goods, therapeutic appliances and equipment (purple bars) and nondurable goods, pharmaceutical and other medical products (orange bars).

During 2020, annual household spending on medical drugs and other products increased at a pace similar to the pace recorded in previous years. And annual household spending on therapeutic products decreased, not unlike it did during the 2007-2009 recession. Both recessions are marked by gray shaded areas in the graph. So, perhaps, broad economic conditions impacting the employment status and income level of households, rather than a pandemic, can best help explain the cyclical decrease in spending in that particular type of goods.

How this graph was created: Search FRED for and select “Real personal consumption expenditures: Medical products, appliances, and equipment.” Next, click on the “Edit Graph” button and use the “Add Line” tab to search for and add the other series. Next, click on the “Edit Line 1” tab, change the units to “Percent change from year ago,” and click on “Copy to all.” Use the “Format” tab to select “Graph type: Bar.”

Suggested by Mickenzie Bass and Diego Mendez-Carbajo.

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