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.

U.S. employment growth in the first quarter of 2026 accelerated slightly to 0.2% relative to one year ago. However, the national average masks significant variation in job growth across the 50 states: 21 of the 50 states had job growth, 28 had job losses, and 1 state (Delaware) had no change relative to one year ago. The median state had a slight job loss of 0.05%.

The FRED map above shows the percent change from a year ago in employment in each state during the first quarter. Nevada and North Carolina showed signs of resilient growth, with the top net job gains of 2.1% and 0.9%, respectively. In stark contrast, the District of Columbia and Maryland had the largest net job loss at 5.2% and 1.7%, respectively.

Metro-level job growth had similar trends as we see in the state-level data, with the median MSA also experiencing no change in employment in the first quarter of 2026 relative to one year ago. The Sierra Vista-Douglas MSA in Arizona had the largest net job loss at -4.6%. In contrast, the Merced MSA in California had the strongest job growth at 3.3%. These numbers tend to vary greatly from quarter to quarter, with even greater sampling errors than the errors at the state and national levels. So, be careful not to read too much into these data.

How these maps were created: Search FRED for “total nonfarm employees in Missouri” (or any other state). Click “View Map” and then “Edit Map.” Change the units to “Percent Change from Year Ago” and the frequency to quarterly with aggregation method “End of Period.” Under “Format,” select “User Defined Method” for how to group the data: Switch the number of color groups to 3 and change the colors to red for states that shed jobs (or a value less than or equal to -0.4), light yellow for states with modest job growth (or less than 0.4), and dark green for states with strong growth (or a value large enough to incorporate the rest of the states). For the second map, repeat the process with an MSA—St. Louis, for example.

Suggested by Charles Gascon and Rehann Silvanus.