Federal Reserve Economic Data

The FRED Blog has discussed interest rates before, including those used as benchmarks of overnight borrowing costs in financial markets. Today, we revisit the topic of overnight financial transactions by comparing the interest rates of two types of loans: secured and unsecured.

The FRED graph above shows two different interest rates paid by financial institutions for borrowing cash at the end of the business day and paying it back at the start of the next business day:

• The blue line shows the overnight unsecured AMERIBOR benchmark interest rate. Reported by the American Financial Exchange, this is a volume-weighted average of interest rates applied to transactions where the borrower does not offer a security as collateral for repayment.
• The red line shows the secured overnight financing rate (SOFR). Reported by the Federal Reserve Bank of New York, this is a volume-weighted median of interest rates applied to transactions where the borrower offers Treasury securities as collateral for repayment.

If you look closely on any given date (we suggest zooming in by clicking and dragging on the graph itself), you will notice that the interest rates are very similar. Despite the fact that overnight loans are paid back very quickly, in less than 24 hours, secured transactions generally record lower interest rates than unsecured transactions: The collateral offered in secured borrowing reduces the amount of potential losses associated with lending, making it cheaper for both lender and borrower.

However, there are days—or even weeks—when unsecured borrowing is cheaper than secured borrowing. You can see here a FRED graph showing the difference between the AMERIBOR and SOFR interest rates. The largest spike of the SOFR series, on September 17, 2019, provides an example. On that date, as described by Sriya Anbil, Alyssa Anderson, and Zeynep Senyuz, a momentaneous shortage in liquidity resulted in a momentous increase in secured borrowing costs and a minimal increase in unsecured borrowing costs. The New York Fed quickly intervened to address this acute need for liquidity, and interest rates promptly returned to their pre-shortage levels.

How this graph was created: Search for and select “Overnight Unsecured AMERIBOR Benchmark Interest Rate.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Secured Overnight Financing Rate.”

Suggested by Diego Mendez-Carbajo.

The unemployment rate is the highest-profile labor market data point, but there are plenty of other ways to gain additional insight into the job market. One such figure is the ratio of unemployed workers to job openings. It’s a straightforward statistic created by combining two key BLS series: unemployment level (via the Current Population Survey) and total nonfarm job openings (via the JOLTS survey).

Exits from the labor force during the COVID-19 pandemic have been analyzed, with research conducted into the reasons for exits and the likelihood of re-entry. It’s led to some debate as to the true tightness of the labor market— after all, total nonfarm payrolls are still 2 million below January 2020 numbers. This ratio sidesteps all that, giving us a measure of labor tightness that reflects firms’ attempts to hire at the present moment.

After reaching a high of 6.5 unemployed workers for every job opening in July 2009, the ratio fell over the next decade to 0.8 in February 2020. It rose to 4.9 unemployed per job opening during the COVID-19 recession, but has since fallen steadily as economic conditions have improved. As of January 2022, it reached the lowest ratio since the BLS began collecting JOLTS data in 2000: just 60 unemployed workers for every 100 job openings. This aligns with reports of widespread labor shortages across several industries; when looking only at the current labor force, there just aren’t enough job seekers for the current level of positions open.

How this graph was created: Search FRED for “Job Openings: Total Nonfarm” (JTSJOL). Set level in thousands, seasonally adjusted. Combine with “Unemployment Level,” thousands of persons, seasonally adjusted (UNEMPLOY). Divide UNEMPLOY by JTSJOL (formula b/a).

Suggested by Nathan Jefferson.

With inflation in the news, we look to the FRED graph above to reveal how much realized inflation has differed from expected inflation. The graph shows one measure of realized inflation from the Bureau of Labor Statistics and measures of expected inflation from the Federal Reserve Bank of Cleveland:

• The blue line shows the monthly year-over-year change in the consumer price index.
• The red line shows one-year-ahead inflation expectations recorded over the course of the year.
• The green line shows the one-year-ahead inflation rate that was expected for February 2022 as of February 2021.

The distance between the blue line’s realized inflation as of February 2022 (7.91%) and the green line’s expected inflation for February 2022 (1.67%) represents an “inflation shock.” These shocks are important for future transactions in the economy.

Let’s use a hypothetical example to explain the actual inflation shock: Suppose that back in February 2021 someone agreed to pay you some nominal amount in exactly one year. At the time, you both expected that the payment would allow you to purchase only slightly less goods and services in February 2022 than you could have purchased when you made the agreement in February 2021, given that some inflation is likely to occur. As noted above, that expected amount was 1.67% less (shown by the graph’s green line).

In reality, as of February 2022, the prices of goods and services had risen 7.91% instead of the expected 1.67% at the time the agreement was made. So the payment received can purchase much less goods and services than what was expected in February 2021: 6.24 percentage points less than what was expected, to be precise. (7.91 – 1.67 = 6.24)

These surprises in inflation change the real value of any nominal contract, including previously agreed-upon salaries, fixed-rate mortgage payments, and government bonds with no built-in inflation protection. To see how inflation surprises affect one of the largest borrowers in the world—the U.S. government—see our On the Economy blog post.

How this graph was created: Search FRED for “Consumer Price Index” and select “Consumer Price Index for All Urban Consumers: All Items in U.S. City Average.” From the “Edit Graph” panel, change “Units” to “Percent Change from Year Ago.” Next, use the “Add Line” tab to search for and select “1-Year Expected Inflation” and click “Add data series.” Change the “Edit Lines” tab to edit Line 2 and change the “Units” to “Percent.” Next, return to the “Add Line” tab to select “Create user-defined line? [+]” and click “Create line.” Set both “Value start/end:” and “to” to be equal to the value of “1-Year Expected Inflation” in February 2021 (i.e., 1.67%). Finally, change the dates at the top right of the graph to be “2021-2-01 to 2022-2-01.” Note that, at the time of this writing, the data in the graph matched the values described in the text; but inflation data are frequently revised, so discrepancies may arise.

Suggested by Yu-Ting Chiang and Jesse LaBelle.