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.

The takeaway

The market value of US Treasury securities is considered a benchmark for setting other borrowing costs, such as mortgages. It’s considered a benchmark because the US government has not failed to make good on its Treasury debt obligations. So it serves as a baseline for determining the value of other types of securities with higher default risks.

Defining the value of Treasury securities

Because of its importance to financial markets, the “market yield on US Treasury securities at 10-year constant maturity, quoted on an investment basis” is one of the most popular series in FRED. Let’s break down what these data measure, piece by piece.

• US Treasury securities are debt obligations issued by the US government. In this case, it is a Treasury note that pays a fixed rate of interest every six months for 10 years. Because it’s backed by the US government, it’s considered a very safe asset to own.
• Market yield is the return you’d earn if you bought the security today at its current market price and held it until you receive its last interest payment. Although the paid interest rate is constant, economic conditions change over time and the daily price of the security changes, too.
• The label “10-year constant maturity” is a way of reporting what a 10-year note, if issued today, would yield. It is calculated using the yields of other actively traded notes.
• “Quoted on an investment basis” means that the yield is reported as an annualized percentage return. That makes it easier to compare with other investments, such as corporate bonds or certificates of deposit.

Recent behavior of the 10-year Treasury yield

Our FRED graph plots data between May 2021 and May 2026. During that time, the lowest yield value was 1.19%, in early August 2021. Since then, yields have risen as high as 4.98% in October 2023, partly reflecting the more-restrictive monetary policy stance adopted by the Federal Reserve and a generally higher interest rate environment. Check out this recent FEDs note by Daniel Covitz and Eric Engstrom to learn more about the factors that shape long-term Treasury yields.

Want to explore more financial data? Check the Board of Governors of the Federal Reserve System H.15 Selected Interest Rates release in FRED.

How this graph was created: Search FRED for and select “Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis.”
Suggested by Diego Mendez-Carbajo.

The takeaway

Firms have intensified their research and development during the AI boom. They’ve reduced their cash holdings, too, likely to pay for R&D. In this post, we use FRED data to explore these trends and the future of AI-related investment.

Background research

Two recent posts from the St. Louis Fed offer insights into AI-related investment:

• A relationship seems likely between rising R&D expenditures and declining cash reserves relative to total assets.
• Significant AI-related investment boosted real GDP growth in 2025 beyond even what IT components contributed during the dot-com boom.

Another analysis projects the AI boom will require $6.7 trillion worldwide in capital expenditures by 2030 to keep pace with the demand for computing power.*

What the data show us

In our FRED graph above, the green dashed line in effect shows the cash holdings of US nonfinancial firms in the corporate business sector as a share of the firm’s assets. We define cash holdings as the sum of checkable deposits and currency, total time and savings deposits, and money market fund shares. (For more about the data, see the Z.1 tables from the Board of Governors’ US Financial Accounts.)

Cash holdings increased from close to 2% in 1990 to close to 4% in the year before the Global Financial Crisis. It increased during that crisis but remained just above 4% in the 2010s. It increased again during the COVID-19 recession, reaching more than 6%. Then it decreased and has fluctuated around 5.5% recently.

With data from the Bureau of Economic Analysis, the solid blue line shows the increase in capital expenditures typically associated with the AI boom. It’s the ratio of private fixed investment in information processing equipment and software to GDP.

The path of this ratio has three distinct phases.

• This ratio surged through the 1990s and peaked at the dot-com crash, followed by a steep decline from 2000 to 2002.
• The ratio remained relatively stable from 2002 through the Great Financial Crisis and into 2023.
• It has jumped sharply from 3.9% in the third quarter of 2023 to 4.7% in the fourth quarter of 2024. It has now surpassed the fourth quarter 2000 peak for the first time.

Future AI investment

As AI-related investment expands further, cash holdings won’t be sufficient to fund it and firms will be more dependent on external financing. Stijn Van Nieuwerburgh argues that the AI buildout has been changing who owns and finances AI infrastructure, as hyperscalers are moving away from fully self-funding data centers and are increasingly combining owned capacity with leased facilities, joint ventures, and partnerships with specialized third-party developers. Monitoring both the adequacy of internal funding and the availability of external finance will be critical for assessing the health of the AI boom.

*“The cost of compute: A $7 trillion race to scale data centers,” April 28, 2025, McKinsey Quarterly, McKinsey & Co.

How this graph was created: Search FRED for and select “TABSNNCB.” Click “Edit Graph”: Use the “Customize” field to search for and select “BOGZ1FL103020005Q,” “TSDABSNNCB,” and “BOGZ1FL103034000Q,” and add the series. Insert (b+c+d)/a in the formula field. Use the “Add Line” tab to search for and select “GDP.” Then add series “A679RC1Q027SBEA” and insert b/a in the formula field. Use the “Format” tab to change the line styles.

Suggested by Masataka Mori and Juan Sanchez.