Federal Reserve Economic Data

Manufacturing is the creation or production of goods with the help of equipment, labor, and chemical or biological processing or formulation. It’s different from mining and construction.

Our first FRED graph, above, tracks the number of employees in these three industries since 1939. After a strong buildup during WWII, manufacturing employment has stayed within a band of 11 to 20 million, with about 13 million currently. There are obvious cyclical fluctuations, but no longer-term trends after its big decline in the first decade of the 21st century. Employment has steadily increased for construction and decreased for mining.

Our second graph divides the same data by the total number of US employees. When we look at each industry’s share of employment in the economy, we get a different perspective: Manufacturing has  steadily declined, construction is stable, and mining has become very small.

Now let’s look at the output of the manufacturing sector. These data don’t go far back, but we can see a marked rise from 1987 to about 2000 and then a flat trend with some cyclical fluctuations. This graph uses an index, which doesn’t say anything about the share of manufacturing output in the economy.

Our last graph tracks the share of manufacturing output in the economy: The data start in 2005 and show the tail end of the decline in the 2000s before it flattens out. Clearly, manufacturing output has done better than manufacturing employment due to an increase in productivity, in part thanks to a move to higher value manufacturing. There may also be very different evolutions for subsectors within the manufacturing industry, as well as long-run trends that any modern economy might experience.

How these graphs were created: Search FRED for the Current Employment Statistics release table and choose Table B-1 (seasonally adjusted); select the series you want and click “Add to Graph.” This the first graph. From the “Edit Graph” panel, for each line add series “All employees, non-farm” and apply formula a/b*100. You have the second graph. For the last two, simply search FRED for “manufacturing output” and “manufacturing value added.”

Suggested by Christian Zimmermann.

Analysts can use several economic indicators to gauge a country’s health. One is the infant mortality rate, which is the number of deaths of infants under the age of one per 1000 live births. The FRED map above shows this rate across the world in 1970.

The second FRED map, above, shows real GDP per capita across the world for the same year. This is a common way of measuring standard of living. We can see an obvious correlation between these two maps, where countries in darker green have both high infant mortality and low real GDP per capita. Does this correlation still hold a half century later?

Our third FRED map, above, shows infant mortality in 2023. We kept the colors consistent with those used for the 1970 map, which allows us to see immediately that the infant mortality rate has decreased notably over time.

Our last map, above, shows real GDP per capita in 2023. While richer countries have lower mortality rates, the map suggests that the correlation between greater wealth and lower infant mortality has changed: The health of some countries has improved substantially over time without much change in their wealth.

How these maps were created: Search FRED for “infant mortality rate” and select any country. Then click on the “Map” button. Select your year, then click on “Edit Map” and customize the interval values as 16, 40, 100, 150, and 250. For the other maps, search for “constant GDP per capita.” Here we changed the map colors so that the better statistics are lighter, as with the first map, and customized our interval values.

Suggested by Dawn Chinagorom-Abiakalam and B. Ravikumar.

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.