Federal Reserve Economic Data

Between 2019 and 2024, consumer prices in the US increased by 26% and the average weekly earnings of all private-sector employees rose by 28%. In other words, average earnings across the country grew 2% faster than nationwide inflation.

This FRED Blog post uses data from the US Bureau of Labor Statistics and recent research by Maximiliano Dvorkin and Cassandra Marks at the St. Louis Fed to discuss regional differences in the change of inflation-adjusted employee earnings during that time.

The FRED graph above shows growth of average hourly earnings between the first half of 2019 and the second half of 2024: The solid blue line tracks the St. Louis, MO-IL, metropolitan statistical area (MSA), and the dashed green line tracks the Atlanta-Sandy Springs-Roswell, GA, MSA. The dollar value of those earnings has been adjusted by their respective regional consumer price indexes and plotted as an index number with a value of 100 at the start of 2019.

The dotted red horizontal line helps compare their change over time. In the St. Louis region, inflation-adjusted earnings increased by 2.6%. In contrast, inflation-adjusted earnings in the Atlanta region decreased by 8.7%.

These differences illustrate the broad range of regional patterns documented by the St. Louis Fed researchers. They used a quarterly census of employment and wages to measure how well hourly earnings kept up with inflation in 21 large cities. In most of those cities, average wages increased more than prices and the regional disparities were mostly due to large differences in the evolution in the price of shelter.

For more about this and other research, visit the publications page of the St. Louis Fed’s website, which offers an array of economic analysis and expertise provided by our staff.

How this graph was created: Search FRED for and select “Average Hourly Earnings of All Employees: Total Private in St. Louis, MO-IL (MSA).” Click on the “Edit Graph” button, select the “Edit Line” tab to customize the data by searching for “Consumer Price Index for All Urban Consumers: All Items in St. Louis, MO-IL (CBSA).” Don’t forget to click “Add.” Next, change the units to “Index (Scale value to 100 for chosen date)” and select “2019-01-01.” Click on “Copy to all.” Next, type the formula a/b and click “Apply.” Select the “Add Line” tab and repeat the previous three steps: search for and add average hourly earnings and consumer price index data in “Atlanta-Sandy Springs-Roswell, GA (MSA),” change the units, and calculate the ratio between both data series.

Suggested by Diego Mendez-Carbajo.

How certain are you of what’s going to happen over the next few months? People’s confidence in anticipating the future dwindles during periods of major economic change. Economists and analysts try to gauge the level of uncertainty about economic conditions because it can affect economic decisions (saving, spending, investing, switching jobs…), which can affect aggregate economic outcomes.

Our first FRED graph above plots one widely used measure of uncertainty: the CBOE Volatility Index, better known as VIX. A post from February explains the VIX in detail. Basically, it uses stock price options to measure how much volatility financial markets expect in the near future, with volatility serving as a proxy for economic uncertainty. This volatility index rose sharply between March and April 2025 and is nearing levels last seen during the COVID-19 pandemic.

Many factors can cause uncertainty, such as structural economic shocks, global events, and financial crises. Policy actions can also drive economic uncertainty. In our second FRED graph above, we use the overall economic policy uncertainty index (EPU) to understand how important policy uncertainty is for the recent increase in overall uncertainty.

The EPU focuses on the economic effects of government policies, rather than financial and economic conditions. It measures this uncertainty from news coverage from a large set of US newspapers. For example, an uncertainty value of 120 would mean that there’s 20% more uncertainty about economic policy than is typical during the base period.

Economic policy uncertainty rises during periods of economic turmoil: It spiked in 1998 during the Asian financial crisis, in 2008 at the onset of the Great Recession, and in 2020 as COVID pandemic lockdowns began. The EPU index began rising persistently in late 2024.

The EPU also has values for subcategories such as taxes, regulation, and trade. By looking at these subcategories, we can identify the specific types of economic policy that are most associated with uncertainty.

Our last FRED graph above plots values for several subcategories as well as overall EPU. Until summer 2024, most types of economic policy uncertainty were at or below 100, meaning they were at normal or low levels. Recent uncertainty, which began rising in 2024, has been especially pronounced for trade policy (pink dashed line). By February 2025, it had jumped to almost 2500, which implies close to 25 times more uncertainty than the norm.

Trade policy uncertainty has spiked before: prior to the signing of NAFTA in late 1993, peaking at just over 1000 before returning to baseline values, and in 2018-19 as China and the US imposed additional tariffs on each other. The most recent increase in trade policy uncertainty corresponds to the announcements of new tariff policies by the Trump administration throughout early 2025. In general, trade policy uncertainty rises during periods of major change in trade policies but returns to lower levels once those policies are established.

How this graph was created: Search FRED for and select the VIXCLS (CBOE Volatility Index) series. For the second graph, search for and select the EPU (economic policy uncertainty) series. For the third graph, search for and select the EPU categorical index, overall series. Then scroll down and click on the release table, below the graph, where you can choose the categories you want to display. Note that this post will continue to be updated with more recent data after its publication.

Suggested by Miguel Faria-e-Castro and Marie Hogan.

The FRED graph above shows two data series on federal government expenditures reported by two different sources: the U.S. Bureau of Economic Analysis (solid blue line) and the U.S. Department of the Treasury, Fiscal Service (dark orange dashed line). Both have similar values but they aren’t identical. Why is that?

In addition to having different titles (expenditures vs. outlays), these two data series differ in their frequency, units, seasonal adjustment, and reporting methodology. Many of these differences are accounted for in the metadata FRED provides in the notes under the graph.

In brief, these differences are as follows:

• The BEA reports Federal Government: Current Expenditures as part of its quarterly Government Receipts and Expenditures data release, which includes the value of gross investment in equipment on a delivery basis and compensation on an accrual basis. Units are billions of dollars, adjusted for seasonal patterns in spending, calculated at an annual rate.
• The Treasury reports Total Federal Outlays as part of its Monthly Treasury Statement, which includes cash flows to fund the operation of different departments and agencies. Units are millions of dollars, unadjusted for seasonal patterns in spending. (The FRED graph uses billions of dollars on an annual basis to make the two series comparable.)

In conclusion, read the “Notes” section under the FRED graph to better tell the story behind the numbers.

How this graph was created: Search FRED for “Federal Government: Current Expenditures.” Next, click on the “Edit Graph” button and select the “Add Line” tab. Search for “Total Federal Outlays” and click on “Add data series.” Customize the data in Line 2 by typing the formula (a/1000)*12 and click on “Apply Formula.”

Suggested by Diego Mendez-Carbajo.