Headlines earlier this year reported that federal US debt exceeded the size of the US economy. Another way to put that: The US debt-to-GDP ratio is over 100%.

Opinions and economic analysis vary about what a sustainable fiscal path looks like in the United States. And the multiple data series in FRED that track the US debt-to-GDP ratio also vary. This post provides some clarification and guidance on understanding this ratio in its many forms and expands the descriptions from a 2025 blog post on this topic.

US public debt and intragovernmental transactions

Our FRED graph above shows three data series that show US debt as a share of GDP: two with 2025 values well above 100% and one with a 2025 value that’s noticeably lower, slightly below 100%. The names of these series are similar, so why are there differences?

Let’s start with intragovernmental transactions. To do their work, various federal government entities engage in financial activities with each other. These transactions affect certain measures of the debt-to-GDP ratio. So we need to disaggregate the components of these measures. Specifically, the two lines with higher values look much more like the third line when we remove federal debt held by agencies and trusts.

Our second FRED graph, above, shows the same three series, after subtracting that component. Here, they look much more similar. In fact, two of them are nearly identical. But the Gross federal debt as percent of GDP series is still noticeably different from the other two. So, our investigation continues…

Fiscal year versus calendar year

FRED provides notes for each series, under the graph. If you read the notes for Gross federal debt as percent of GDP, you’ll see this ratio is sourced from the gross federal debt series, which comes to FRED from the Council of Economic Advisers and follows the federal government’s fiscal year, not calendar year. (The federal fiscal year is Oct. 1 through Sept. 30 and has been for around half a century.) But the other two debt-to-GDP ratio series (GFDEBTN and FYGFDPUN) collect their debt figures from US Treasury Bureau of the Fiscal Service datasets, which follow the calendar year. Hence, the slight difference.

More debt-to-GDP pitfalls

In economics, US federal government debt is called a stock variable, whereas GDP is a flow variable. Stock variables are defined at a singular point in time, such as the end of the month. Some stock variables may actually be measured at the beginning of the period.

Flow variables like GDP provide observations per unit in time, such as per quarter or per year. So, when creating ratios that combine both these variables, one must be careful to understand precisely what the numerator and denominator are capturing. For example, the Gross federal debt as percent of GDP series has a fiscal-year numerator and a calendar-year denominator. In short, reading the notes matters!

How these graphs were created: Search FRED for series ID GFDEGDQ188S and click on the result. Select “Edit Graph” and modify the frequency to “Annual.” Use the “Add Line” tab to search for series ID GFDGDPA188S and click “Add data series.” Repeat to add FYGFGDQ188S. Return to the “Edit Lines” tab, select Line 3 (FYGFGDQ188S), then modify the frequency to “Annual.” From the “Format” tab, open the “Customize” box for all three lines. Adjust line style, line color, and other settings as you wish. Update the time range to begin 1981-01-01 and end 2025-01-01. For the second graph, start with the first graph. From “Edit Graph,” select Line 1 and enter HBATGDQ188S into the search bar under “Customize data.” Click “Add.” In the “Formula” bar, enter a-b and click “Apply Formula.” Switch to Line 2 under the “Select Lines” dropdown and repeat the subtraction process.

Suggested by Scott St. Louis and Christian Zimmermann.

The Federal Reserve has a dual mandate from Congress: stable prices and full employment.

For the first objective, what prices should the Fed keep stable? There are many to choose from. Although they’re obviously correlated, they do deviate from each other, especially in the short run.

The Fed and specifically the FOMC look at many price indexes, but their preferred measure is the personal consumption expenditures (PCE) price index. The inflation rate from this index is published monthly by the Bureau of Economic Analysis. Its advantage over the consumer price index (CPI) and the producer price index (PPI), for example, is that the PCE covers a broader set of household expenses.

Although monetary policy aims at price stability, it does not have an instantaneous effect on prices. Policy is believed to follow long and variable lags, on average, over a couple of years. So it’s really important to have a measure of prices that can be well predicted, as the FOMC is trying to influence future prices. For this reason, the FOMC primarily focuses on core PCE inflation. Core PCE inflation excludes food and energy because those two types of prices can fluctuate dramatically, because of seasonal factors or the high volatility of markets. Given the lags that the FOMC has to work with, this kind of volatility makes forecasting the path of prices much more difficult. And the FOMC is not in a position to react to short-term price fluctuations anyway.

The FRED graph above shows three series: core PCE inflation, PCE food inflation, and PCE energy inflation. It’s clear that core PCE is much more stable, while the other two fluctuate widely around it. One could modify this graph to show month-to-month inflation instead of year-to-year inflation, and that picture is even starker. In engineering parlance, the signal-to-noise ratio is much better with core PCE inflation.

How this graph was created: On FRED, find the release table for PCE price indexes by major type of product. Check the three series and click “Add to Graph.” Then click “Edit Graph,” choose units “Percent change from year ago,” and click “Apply to all.”

Suggested by Christian Zimmermann.

The takeaway

Labor is used in production, and measuring the costs of that labor is important for business decisionmaking. There are several ways to measure these costs, and it’s important to know their differences.

Three forms of labor cost data

To understand the changes in the cost of labor, researchers commonly use one of three time series: hourly wages, the employment cost index, and unit labor cost.

Average hourly wage of workers is the first and most obvious, shown by the solid blue line in our FRED graph above. It’s the percent change from a year ago of average hourly earnings for all private employees. The line generally hovers around 2.5%, but it sharply increased in early 2020 at the time of the COVID-19 recession. In recent quarters, the line has been between 3.5% and 4%.

This series has two drawbacks. First, it doesn’t account for compositional changes that occur when the economy slows. Research from the St. Louis Fed showed that low-wage workers have been more likely to lose their jobs than high-wage workers; average hourly earnings drastically shifts up as the economy loses lower-wage workers. Second, average hourly earnings doesn’t consider other employment benefits such as healthcare, pensions, and bonuses.

The Employment Cost Index (ECI) doesn’t have these drawbacks. It’s the change in hourly labor cost to employers, shown by the solid green line in our FRED graph. According to the BLS, “The ECI uses a fixed ‘basket’ of labor to produce a pure cost change, free from the effects of workers moving between occupations and industries.” By including both wages and benefits in its calculation, the ECI gives us a better picture of total compensation as well. During the COVID-19 recession, the ECI trends downward, as it does not suffer from the compositional changes noted previously.

Unit labor cost (ULC) is our third measure, shown by the dashed orange line. It measures the ratio of hourly compensation to labor productivity, capturing how much it costs employers to produce a unit of output. Like the ECI, it includes both wages and benefits. It also accounts for productivity changes: When workers produce more output per hour, labor costs effectively decline. In the graph, we see ULC is more variable over time than the other two measures. In recent quarters, ULC growth has fallen below the other measures because of solid labor productivity growth.

How this graph was created: Search FRED for and select “Average Hourly Earnings of All Employees, Total Private.” Click “Edit Graph,” adjust units to “Percent Change from Year Ago,” and change frequency to “Quarterly.” Click “Add Line,” search for “Employment Cost Index: Total compensation,” and select the private industry series. Click “Add Line” again and search for “Unit Labor Costs for All Workers” in the nonfarm business sector. Change the start date of the graph to January 2002. Click “Format” to change line styles: Line 2 “solid,” Line 3 “dash,” and all line widths 2.

Suggested by Serdar Birinci and Gus Gerlach.