Federal Reserve Economic Data

On December 4, 2024, the Bureau of Economic Analysis released their 2023 real GDP breakdown. Here are some highlights from the data set, some of which are shown in the FRED map above:

• In 2023, 91% of metropolitan statistical areas (MSAs) expanded and only 9% contracted.
• Nationally, real GDP increased by 2.9%.
• Five MSAs grew at a similar rate as the national average, including the large MSAs of Phoenix-Mesa-Scottsdale, AZ, and Pittsburgh, PA.
• The MSA with the most growth was Midland, TX, at 42.9%.
• The MSA with the least growth was Elkhart-Goshen, IN, at -9.3%.
• The largest MSA in the St. Louis Fed’s 8th District is St. Louis, which grew by 2.5%, which placed it at 178th in the nation or right at the 50th percentile among MSAs.

The FRED map above digs deeper, into the county level. The county with the most growth was Throckmorton, TX, at 125.8%, and the county with the least growth was Lincoln County, WA, at -39.6%. Since both of these counties are very small and not a part of an MSA, GDP can fluctuate greatly from one year to the next. Growth is also not uniform for the counties of an MSA. Using the St. Louis MSA as an example in the map above, 6 of the 15 counties in the St. Louis metro area experienced negative growth while 9 experienced positive growth.

There are many reasons why some counties grow while others contract. For example, the industrial composition can amplify the degree of expansion or retraction in relation to the national overall business cycle. Demographic makeup and migration patterns of a county also can be a factor. These reasons are  explored in more detail in this St. Louis Fed essay.

How these maps were created: First map: Search FRED for “Real GDP MSA” and click on the first choice. Click on the green “View Map” button and then the orange “Edit Map” button. Change units to “Percent Change from Year ago.” Then, switch the number of color groups to 2, the data grouped by to “User Defined Method” and then define the scales at 0 and 50. For values less than 0 choose red to show contraction and values less than 50 choose green to show expansion.
Second map: Repeat the exercise with “Real GDP county,” but define the scales at 0 and 5. All St. Louis area counties were under 5, which helps focus on them more closely.

Suggested by Jack Fuller and Charles Gascon.

The FRED Blog has discussed the income and wealth gains from graduating college and how to pay for a college education using a 529 saving plan. Today, we discuss how the type of college a student attends impacts their future earnings.

The FRED graph above shows US Bureau of Labor Statistics data on the income of an average college graduate. That income is shown in proportion to the income of an average high school graduate, which makes it easier to see the impact of higher education. Between 1996 and 2012,* on average, college graduates earned twice as much income as high school graduates.

However, this average doesn’t fully describe the experiences of different groups of college graduates. For insights on this topic, we need to refer to some recent research:

Oksana Leukhina and Mickenzie Bass at the Federal Reserve Bank of St. Louis find that income among college students varies widely according to the type of institution they attended. With data from the US Department of Education, they show that median students from the most selective schools earn 75% more than median students from the least selective schools.

Moreover, they find that college characteristics such as the amount of money spent in educating each student and the share of students majoring in science, technology, engineering, and mathematics (STEM) can explain a substantial share of the earnings gap between different groups of college graduates.

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.

*Note: These BLS data come from an older, discontinued series; the newer series is currently being updated in FRED.

How this graph was created: Search FRED for and select “Income Before Taxes: Wages and Salaries by Education: Bachelor’s Degree.” Click “Edit Graph” and select the “Edit Line” tab to customize the data by searching for “Income Before Taxes: Wages and Salaries by Education: High School Graduate.” Don’t forget to click “Add.” Next, type the formula a/b and click “Apply.”

Suggested by Diego Mendez-Carbajo.

The blue line in the FRED graph above shows life expectancy at birth in sub-Saharan Africa relative to life expectancy in the United States. In the early 1960s, life expectancy in sub-Saharan Africa was 60% of what it was in the United States.

The general upward progress shown by the blue line indicates that, from the 1960s onward, sub-Saharan Africa was on a convergence path toward a life expectancy similar to that of the United States.

The HIV/AIDS epidemic of the 1980s and early 1990s set sub-Saharan Africa back 40 years on its path to convergence: The green line shows the pre-1980 trend sub-Saharan Africa was following prior to the HIV/AIDS epidemic: The blue line departs from the green line in the early 1980s and returns to it circa 2020.

Two key observations:

1. A plateau: The flat portion of the blue line, spanning the early 1980s to late 1990s, corresponds to the peak of the HIV/AIDS crisis, which affected Sub-Saharan Africa’s life expectancy more severely than US life expectancy during this period.
2. An accelerated recovery: From the late 1990s onward, Sub-Saharan Africa experienced a faster rate of life expectancy increase compared with its pre-epidemic trend. This accelerated recovery has allowed the region to catch up to its pre-1980 trajectory.

Two questions:

1. Why did the HIV/AIDS epidemic affect Sub-Saharan Africa so much?
2. Why did Sub-Saharan Africa’s life expectancy accelerate and eventually return to its trend, after the epidemic?

A large literature already exists on the first question, including this article. But we have no references right now to help answer the second question.

How this graph was created: Search FRED for “Life Expectancy at Birth, Total for the United States.” Click on “Edit Graph.” Under customize data find “Life Expectancy at Birth, Total for Developing Countries in Sub-Saharan Africa.” Click on “Add.” Under Formula enter b/a*100 and “Apply Formula.” Go to the “Add Line” tab, click on “Create User-Defined Line” then “Create Line.”

Suggested by Guillaume Vandenbroucke.