Federal Reserve Economic Data

On February 5, 2026, the Bureau of Economic Analysis released their 2024 real GDP breakdown at the county level. Here are some highlights from the data set, some of which are shown in the FRED map above:

• In 2024, real GDP growth was positive in three-quarters of all counties.
• Nationally, real GDP increased by 2.8%. However, the median county experienced growth of 2.3%. About two-thirds of counties experienced growth ranging from -1.6% to 6.0%.
• The county with the fastest growth was Carter County, Montana, at 76.6%.
• The county with sharpest decline was Baca County, Colorado, at -46.3%.
• There was a positive relationship between real GDP growth and the size of the county. Among the largest 10% of counties, growth averaged 3%; whereas, among the smallest 10% of counties, growth averaged -1.5%.
• The county with the fastest growth here in the St. Louis, Missouri-Illinois metro area was Madison County, Illinois, with 8.3% growth. Jersey County, Illinois experienced the slowest growth in the metro area, at -1.2%.

As noted above, there are some large numbers for growth and contraction of real GDP at the county level. This is because many counties are very small. Therefore, GDP can fluctuate greatly from one year to the next: Economic shocks such as a business openings or closings in a small town can have a significant impact on the community and, thus, the economic data. There are many reasons why some counties grow while others contract. For example, the industrial composition can amplify the degree of expansion or contraction in relation to the national overall business cycle. Demographic makeup and migration patterns of a county can also be a factor. These reasons are explored in more detail in this St. Louis Fed essay.

How this map was created: Search FRED for and select “Real GDP County” and click on the first choice. Click on the “View Map” and then “Edit Map” buttons. Change units to “Percent Change from Year Ago.” Then switch the number of color groups to 3 and “data grouped by” to “User Defined Method”; then define the scales at 0, 3, and the highest value (which is 77). For values less than 0, choose red to show contraction; for values less than 3, choose light green to show slow to moderate expansion; for values less than 77, choose dark green to show rapid expansion.

Suggested by John Fuller and Charles Gascon.

FRED recently added 7 data series about the business conditions and outlook of upstream oil and gas energy firms headquartered in the 11th Federal Reserve District.

In industry lingo, upstream refers to oil and gas exploration and production and the related services that support those activities. Geographically, the 11th District of the Federal Reserve consists of Texas, northern Louisiana, and southern New Mexico. The regional Reserve Bank in the 11th District is in Dallas, Texas, so the dataset itself is called the Dallas Fed Energy Survey.

The FRED graph above shows the three broadest indicators of business conditions captured by the survey:

• level of business activity (solid blue line)
• company outlook (dashed green line)
• uncertainty (dashed orange line)

The data are reported as diffusion indexes. You can read about another example of this type of index here. In short: The direction of change in the value of the index indicates rising or falling values of the underlying concept being assessed.

What do the indexes show?

The indexes of company outlook and level of business activity generally move in the same direction and at the same time. Between Q3 2024 and Q3 2025 (the last four observations available at the time of this writing), those indexes ranged between 7.1 and -17.6. That suggests relatively stable outlook and activity conditions. However, the index measuring uncertainty was above 40 during most of that time. This is noteworthy because, since 2016, when data are first available, the uncertainty index generally moved in the opposite direction of the indexes of business activity and company outlook. Perhaps that could be expected because oil and gas exploration and production activities are large scale and expensive operations that take many years to plan and execute. In short: Uncertainty undermines this industry.

How this graph was created: Search FRED for and select “Dallas Fed Energy Survey – Level of Business Activity.” Click on the “Edit Graph” button and select the “Add Line” tab to search for “Dallas Fed Energy Survey – Company Outlook.” Don’t forget to click on “Add data series.” Repeat the last two steps to search for and add “Dallas Fed Energy Survey – Uncertainty.”

Suggested by Diego Mendez-Carbajo.

In a recent FRED Blog post, we discussed how confidence intervals show the level of certainty about the accuracy of an estimate. In short: Wider confidence intervals signal more uncertainty.

Also, larger survey sample sizes increase the statistical accuracy of the data collected and allow data users to confidently compare apples to apples. Today’s post offers a bite-size example.

Our FRED graph above shows US Census estimates for median household income in three US counties:

• Pitkin, CO (solid blue line), home to the town of Aspen.
• New York, NY (dashed red line), the borough of Manhattan in New York City.
• Teton, WY (solid green line), home to the town of Jackson in the Jackson Hole valley.

Between 1989 and 2023, estimated median household income was frequently very similar for all three locations listed above: the coastal urban center and the two mountainous rural areas. But the number of residents was vastly different.

Population size influences the number of households sampled to collect income data: The more populous the county, the more households are sampled. So the estimated data are relatively more precise in more populous counties (e.g., New York County) than in less populous counties (e.g., Pitkin and Teton counties).

FRED also has data that capture the confidence intervals reported along with the estimated income data. The relative confidence interval for New York, NY, data can be as much as five times smaller than those for the Pitkin, CO, and Teton, WY, data, as seen in the graph below. That means far less uncertainty about the accuracy of the reported figures.

So, clearly, it’s best to be cautious when you try to make fair apples-to-apples data comparisons, including data from the Big Apple itself.

How these graphs were created: Search FRED for and select “Estimate of Median Household Income for Pitkin County, CO.” Click on the “Edit Graph” button and select the “Add Line” tab to search for “Estimate of Median Household Income for New York County, NY.” Don’t forget to click on “Add data series.” Repeat the last two steps to add the third series: “Estimate of Median Household Income for Teton County, WY.” Lastly, use the “Format” tab to customize the line styles. For the second graph, follow the same general procedure, except that each line is now composed of three series: Search first for “90% Confidence Interval Upper Bound…,” then “90% Confidence Interval Lower Bound…,” and finally the above mentioned estimate. Then apply the formula (a-b)/c on each line.

Suggested by Diego Mendez-Carbajo.