Data providers may use the same labels for their data even if their methods of collecting the data differ. FRED can help you compare and understand these differences.

Our FRED graph above displays two types of mortgage rates from two different sources: the weekly 30-year and 15-year fixed mortgage rates reported by Freddie Mac (solid lines) and Bankrate (dashed lines). Both data sets show similar interest rates, even though the sources use different methodologies to collect their data. Freddie Mac calculates the average rate on “thousands of mortgage loan applications” from lenders across the country when a borrower applies for a loan. Bankrate reports data from a survey of the “10 largest banks and thrifts in 10 large US markets.”

Use FRED’s graphing features to compare data series visually: This FRED graph shows the entirety of both series, and this FRED graph adds a formula to reveal the differences between the two series, which at times were notably pronounced. You can also download data from FRED to compare series quantitatively and read the FRED series notes to better understand the collection methods used.

We provide another interest rate data comparison for researchers and sleuths with the set of benchmark data from the G.19 Consumer Credit release reported by the Board of Governors of the Federal Reserve System:

• Auto loans at 48 months and 60 months
• Credit cards
• Personal loans

Here are all the recently added series from the Bankrate Monitor National Index and a link to an earlier post discussing recent patterns in interest rates on bank accounts.

How this graph was created: Search FRED for and select “30-Year Fixed Rate Mortgage Average in the United States.” Click on the “Edit Graph” button and select the “Add Line” tab to search for “Bankrate Monitor (BRM): Fixed Mortgage Rate – 30 Year Fixed.” Don’t forget to click “Add data series.” Repeat the last two steps to add data on “15-Year Fixed Rate Mortgage Average in the United States” and “Bankrate Monitor (BRM): Fixed Mortgage Rate – 15 Year Fixed.”

Suggested by Diego Mendez-Carbajo.

Our FRED graph above shows that unemployment is almost always doing one of two things: (1) declining slowly during expansions or (2) rising rapidly during recessions. Friedman (1964, 1993) compared this behavior to playing a musical instrument, describing it as a “plucking model” of unemployment.

Over the past 3 years, however, the unemployment rate has done something it almost never does: It has risen slowly from a low level, but there has been no sharp rise accompanied by a recession.

Our non-FRED graph below* illustrates how unusual this behavior is.

• The jagged red line is the US civilian unemployment rate.
• The smooth green line is a 25-month moving average of the unemployment rate that smooths out small movements in the rate.
• The green triangles show the minima of this moving average.
• The blue circles show the unemployment rate 36 months after the minimum.

In nearly every case —except 2020 (COVID) and 2023 — the unemployment rate climbs significantly in the 36 months after a local minimum. 2020 was exceptional because the huge COVID-related spike in unemployment rose and fell within 36 months. The past three years (2023-2026) have also been exceptional in that unemployment has risen very modestly and slowly from a very low level, but there has been no recession and no sharp uptick in unemployment.

It’s not obvious how to explain this very unusual behavior, but recent economic activity has been extraordinary. The 2020 recession associated with COVID was unprecedented, and the accompanying fiscal and monetary stimuli were quite large, leaving many consumers flush with cash. Unemployment rose to record levels, and then it declined at a record rate to a very low level. The 2020 recession was followed by supply shocks from the Russian invasion of Ukraine in 2022. In short, strange inputs tend to produce strange outcomes.

How these graphs were created: First graph: Search FRED for and select “unemployment rate.” *Second graph: FRED’s a great graphing tool, but can’t do everything: You can import data from FRED to your favorite application and create custom graphs like this one, which displays moving averages and only selected data points.

Suggested by Christopher Neely.

The takeaway

The velocity of money is how fast money travels from one entity to another to purchase goods and services in a given period. Since 2008, velocity in India has been closing the gap with velocity in the US.

The invisible pulse of the economy

Economists often rely on what can be directly measured to understand the economy. Examples include the unemployment rate or how much output an economy produces. Less-visible or “indirectly observed” metrics can also reveal insights about the economy. These types of metrics can provide information on economic activity that adds to information provided by direct measurements, such as GDP.

The velocity of money is one of these indirect metrics. It is the rate at which money travels from one entity to another—that is, the number of times a unit of currency is used to purchase goods and services in a given period. Although it cannot be observed directly, it can be computed as the ratio of nominal GDP to the money stock. High velocity indicates an active economy, while low velocity indicates a stagnant one.

Our FRED graph above illustrates the velocity of money in two economies, the United States and India, from 2004 to 2019. And the graph tells a clear story. In 2004, money in the US was circulating twice as fast as that in India: A US dollar was used about 2.6 times to purchase goods and services, whereas an Indian rupee was used 1.3 times.

A notable shift began around the first quarter of 2008, near the onset of the Great Financial Crisis: US velocity started to decline. By the fourth quarter of 2019, US velocity had fallen to 1.6, reflecting a slowdown in economic activity, while India’s stood at 1.35.

How is velocity measured?

These two economies use a monetary aggregate called M1 to represent the stock of money that can most easily be used for transactions. Typically, it consists of cash in circulation and deposits in checking and savings accounts. M1 in both countries currently includes deposits in savings accounts, but India’s M1 has done so for a longer period of time, since their savings accounts have been regularly used for everyday payments.

M1 in the US didn’t include savings deposits until 2020. A change in regulation in 2020 made the deposits in savings accounts a part of M1. So, for an apples-to-apples comparison of velocities, we add savings deposits to US M1. This gives us a harmonized metric to compare the pulses in the two economies.

How this graph was created: Search FRED for and select the seasonally adjusted series for “Gross Domestic Product” and click “Edit Graph.” In the “Edit Line” tab, use the “Customize data” field to search for and select the seasonally adjusted series for “M1” (click “Add”) and “Savings Deposits: Total (DISCONTINUED)” (again, click “Add”). In the formula field, type a/(b + c) and select “Apply Formula.” Click the “Add Line” tab and search for and select the seasonally adjusted series “National Accounts: GDP by Expenditure: Current Prices: Gross Domestic Product: Total for India” and click “Add.” Using the “Edit Lines” tab, in the “Customize data” field, search for and select seasonally adjusted series for “Monetary Aggregates and Their Components: Narrow Money and Components: M1 and Components: M1 for India” and click “Add.” In the formula field, type a/b and select “Apply Formula.” Finally, select “2004-01-01” to “2019-12-01” as the time frame.

Suggested by Kritika Chakrabarti, B. Ravikumar, and Debargha Som.