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.

A metropolitan statistical area (MSA) is a grouping of counties, cities, and towns with a high degree of social and economic integration. At the time of this writing, there are 387 such geographies in the United States.

The definitions and boundaries of MSAs are revised periodically to account for shifting patterns in resident population and economic conditions. So, some redrawing of MSA boundaries can shrink their geographies and/or reduce their population counts.

The solid blue line in the FRED graph above shows the annual resident population in the Charleston, West Virginia, MSA between 2000 and 2024. The other two lines represent the counties that made up the MSA during two different periods of time:

• The green dashed line shows the population for Kanawha, Jackson, Boone, Lincoln, and Clay counties, which is what the Charleston MSA included between 2010 and 2020.
• The orange dotted line shows the population for just Kanawha, Boone, and Clay counties, which is what the Charleston MSA included after 2020.

In both cases, when the number of counties in the MSA decreased, the solid blue line showing the MSA population dropped in a stepwise fashion. To learn more about this topic and how different data sources reflect changes in economic boundaries, read this earlier FRED Blog post.

How this graph was created: Search FRED for and select “Resident Population in Charleston, WV (MSA).” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Resident Population in Kanawha County, WV.” Next, customize the data in Line 2 by searching for “Resident Population in Boone County, WV.” Click on the “Add” button. Repeat the last step to add “Resident Population in Clay County, WV.” Next, type the formula a+b+c and click “Apply.”

Suggested by Diego Mendez-Carbajo.