Federal Reserve Economic Data

When a commercial bank borrows from its District Federal Reserve Bank, it is using the Fed’s discount window. As described by the Board of Governors, this lending program provides commercial banks with short-term liquidity to support the smooth flow of credit to households and businesses. The operation of the discount window has evolved in response to the changing needs of the economy and financial system, and this FRED Blog post looks at its history through the lens of data.

The FRED graph above shows the interest rate that financially sound commercial banks pay when borrowing from the Federal Reserve. It is called the “discount window primary credit rate” because it offers the best credit terms to qualifying depository institutions. The daily data series starts on January 9, 2003, when the primary and secondary discount window program started making liquidity available to more commercial banks under different terms and at different costs. The source of the data is the Board of Governors.

But the discount window has been in operation since 1914, when the Federal Reserve System was established. Do you want to know how this tool of monetary policy operated further back in time? Keep on reading.

Before the primary and secondary credit programs were put in place, the Fed determined access to short-term liquidity for commercial banks through the adjustment and extended credit programs. There was and still is a third, seasonal  credit program not discussed here. The FRED graph above adds a second line (in red) to our initial graph. It shows the monthly average discount rate on loans to member banks and it extends the data back in time to January 1950. Note that the data source is listed as the International Monetary Fund, but the original data are reported by the Board of Governors through the Data Download Program.

Between 1914, when the Federal Reserve System was established, and the first half of 1922, when Federal Reserve District banks started buying large amounts of government securities in the open market, management of the discount window was intended as the principal instrument of central banking operations.

The FRED graph above adds a third line (in green) to our second graph. It shows the values of the basic discount rate that the Federal Reserve Bank of St. Louis charged to its member banks between November 16, 1914, and the launch of the primary and secondary discount window program in 2003. Notice that the data have no consistent frequency, as they were recorded only when the interest rate changed. Subject to approval by the Board of Governors, each of the 12 Reserve Banks in the Federal Reserve System can set its own discount rate. In practice, since the 1930s, the rates of the 12 Reserve Banks have rarely differed, and then only for a day or two. However, before 1933, there were more often differences in the rates across the Banks.

How these graphs were created: To create the first graph, search for and select “Discount Window Primary Credit Rate.” To create the second graph, from the “Edit Graph” panel, use the “Add Line” tab to search for and select “Interest Rates, Discount Rate for United States.” To create the third graph, repeat the last step to add “Federal Reserve Bank of St. Louis Basic Discount Rate (DISCONTINUED)” to the graph.

Suggested by Diego Mendez-Carbajo.

How much more do managers earn than the workers they manage? Sometimes the data can answer a question like this directly. But in this case, we must do a little work.

First, our investigation today is motivated by the fact that earnings for non-managers are persistently lower than earnings for the total pool of employees, which includes managers. But we don’t have data for the earnings of managers alone. A few developments during the pandemic make this question even more interesting: Non-manager weekly earnings rose when the pandemic hit, but non-manager hourly earnings (rate of pay) did not rise when the pandemic hit. So, did non-managers work more hours for the same rate of pay? Let’s see.

We compare weekly earnings for (i) the entire pool of workers and (ii) just non-managers so we can try to suss out what managers make. Non-managers earn about $170 per week less than the pool of all workers (which includes managers), and the graph above shows no visible evolution in that difference.

Our second FRED graph compares the same datasets but in terms of percent change from a year ago. We see no systematic difference between the two until the pandemic hits. At that point, non-managers started making significantly greater gains than the average of all workers; and they continue to do so.

Now, “weekly earnings” are a product of hourly pay and weekly hours. So, we focus on hourly pay in the graph above—again, in terms of percent change from a year ago. We see that non-manager pay didn’t immediately increase more once the pandemic hit. The increase only in the latter part of the pandemic seems to imply weekly hours must have increased more in the early stages of the pandemic. Did they?

We cannot see any difference at all in the two lines showing weekly hours. How is that possible? We would have expected the blue line to be significantly higher than the red line early in the pandemic. The problem is that the hours measured in the first graph aren’t the same as those in the last graph. The first uses the concept of average hours, which essentially represent regular work hours for an employee. The last graph considers aggregate hours, which is a total of all hours worked in the economy, and thus multiplies the average hours by employment. And the latter changed quite a bit during the pandemic. So, as it turns out, the decomposition we wanted to perform here doesn’t work. Which is why it’s so important to understand the precise definitions of the data you’re working with.

How these graphs were created: Search FRED for “average weekly earnings” and sele ct the production and non-supervisory workers series. From the “Edit Graph” panel, search for the same seriesbut pick “all employees.” You have the first graph. For the second, set units to “percent change from previous year” and apply to all. For the third and fourth graphs, repeat the operations with “average hourly earning” and “aggregate weekly hours.”

Suggested by Christian Zimmermann.

This post offers something old and something new: It covers similar ground from a post last November, but it’s the first post to use FRED’s new mapping feature! We hope you like the look and functionality!

Our splendid new map above depicts growth in new business creations by state during 2021—specifically, the percent change between 2020 and 2021 in the number of business applications recorded by the U.S. Census for each state. Darker colors represent higher growth rates.

The data themselves also offer something old and something new: Growth in business applications was highest for the first state to enter the Union and lowest for the next-to-last state to enter the Union.

In Delaware, known as the “First State,” businesses grew by 47%, which is almost double the typical (or median) growth rate of 25%. Business laws there have long facilitated the formation, or incorporation, of new commercial enterprises, so perhaps this is no surprise.

However, in Alaska, on the opposite side of the continent, their 2% growth in business applications was the lowest in the country. Here at the FRED Blog, we shall not wander “into the wild” speculating about the reasons behind the slow growth in new business creations in the “Last Frontier” state.

How this graph was created: Search for and select “Business Applications for Alaska.” From the “Edit Graph” panel, use the “Edit Line 1” tab to modify the data frequency to “Annual” and the aggregation method to “Sum.” Last, close the edit panels and click on the “View Map” green button. Happy travels with FRED maps!

Suggested by Diego Mendez-Carbajo.