Federal Reserve Economic Data

Earlier this month, the Board of Governors of the Federal Reserve System announced it was making available additional funding to eligible depository institutions through a new Bank Term Funding Program. FRED quickly added four series with those data.

The program provides liquidity to US banks, saving associations, and credit unions to ensure those financial institutions have funding at hand to meet the needs of all their depositors. Borrowers pledge US Treasuries, agency debt and mortgage-backed securities, and other qualifying assets as collateral. If needed, the Department of the Treasury would provide $25 billion as credit protection, or backstop, to the Fed.

The FRED graph above shows the dollar amount borrowed through that term program during its first two weeks of operation. The complete time series of data is contained on the FRED series page, which updates automatically whenever new data become available.

Now, this is called a term program because the lending window is finite and, specifically, is set to close on March 11, 2024. After that date, when the Federal Reserve Banks are paid back, the FRED series will stop updating and include the label “DISCONTINUED.”

The Fed establishes these term programs to alleviate short-term pressures in financial markets. One earlier example is described in this Economic Synopses essay by David Wheelock on the workings of the Term Auction Facility between December 2007 and February 2008.

How this graph was created: Search FRED for “Assets: Liquidity and Credit Facilities: Loans: Bank Term Funding Program, Net: Wednesday Level.”

Suggested by Diego Mendez-Carbajo.

In our previous blog post, we discussed how the interpretation of data can be strongly influenced by the price index you choose to deflate those data—that is, when you want nominal measurements in real terms.

The FRED graph above shows the three price indexes that were used in that previous post. When you look at them over several decades, you notice that they show stark differences. First, the consumer price index (CPI) has increased significantly more than the GDP deflator since the early 1970s, opening a gap of almost 30%. The producer price index (PPI) fluctuates strongly between the two, being generally closer to the GDP deflator.

Why these large differences? Well, they do measure different things…

• The CPI measures the evolution of the prices for a basket of goods a typical urban household would consume.
• The GDP deflator measures the overall price of all that is produced in the economy.
• The PPI measures the price that producers are getting for their wares.

At first glance, they seem to be measuring essentially the same things—all that is consumed, produced, and sold. A closer look reveals some important differences. The CPI, by definition, includes only consumption goods and services. So, it includes imports but not exports. The other two indexes include exports but not imports. The GDP deflator also includes investment goods and public expenses. The PPI covers goods but not services.

How the prices of these included or excluded categories have evolved over the decades since the 1970s can generate gaps. For example, there are now much more exports and imports than there were 50 years ago. Energy is much more expensive and factors-in differently for the three indexes. The proportion of services in consumption and output has increased a lot, too. And that’s why this FRED graph looks the way it does.

How this graph was created: Search FRED for CPI, click on “Edit Graph,” open the “Add Line” tab, add “GDP deflator, then “PPI.” Choose units index 100 as of 1970-01-01, click “Apply to All,” and start the data on 1947-01-01.

Suggested by Christian Zimmermann.

The FRED graph above shows a disturbing pattern: Since the early 1970s, there’s been an apparent disconnect between labor productivity and real wages. (The accumulated difference was 70% at the end of 2022.)

Our goal here is to better understand these statistics, so let’s first define what we’re talking about.

• Labor productivity is computed by taking the ratio of total production in the economy (i.e., real GDP) to total number of hours worked in the economy.
• Real wages is computed by taking total compensation paid to non-farm employees and dividing it first by an estimate of total number of hours worked and then by the consumer price index, thus providing an idea of the purchasing power of an hour of work.

This graph is disturbing because it seems to show that US workers only partially benefitted from the increases in their productivity since the 1970s. This decoupling isn’t unique to the United States, however, so let’s look more closely at what’s behind the data.

First, these averages may hide much of what’s going on across the distribution of wages—in particular, the sectoral composition of the economy. For example, there was high productivity growth over the past decades in some sectors, such as finance and information, that wasn’t matched by labor compensation, despite substantial increases. And there was no decoupling at all in other industries.

Second, the price index we use matters. So we replicated the graph above with two more series: Labor compensation is now deflated by the GDP deflator and by the producer price index (PPI).

The results are quite different, and the decoupling isn’t as stark. The original real labor compensation line describes what purchasing power workers have, as it is deflated by a price index that tracks a typical basket of goods a household would buy. The two new lines look at this from the employer side: How much are workers paid compared with (i) the value of all things produced (GDP deflator) and (ii) the prices that producers are getting for their wares (PPI)? These two lines are much closer to the productivity line, indicating that the choice of prices matters.

Why would you take one price series over another? It depends on what question you’re asking of the data. If you want to understand how businesses decide to allocate resources to factors of production (labor, capital, intermediates), then the second graph is relevant. If the topic is about worker purchasing power, then the first graph is relevant. But more fundamentally, why would these price indexes differ so much? That’s the topic of our next blog post…

How these graphs were created: For the first graph, search FRED for “hourly compensation” and select the non-farm series. Click on “Edit Graph,” add a series by searching for the CPI, apply formula a/b, and change the units to a custom index with 100 as of 1970-01-01. Now open the “Add Line” tab and look for labor productivity. Apply the same custom index. For the second graph, take the first and add two more lines, using the same steps to add the GDP implicit price deflator and the PPI (instead of the CPI).

Suggested by Christian Zimmermann.