Federal Reserve Economic Data

FRED has data for various segments of the US labor force, including employment of native-born and foreign-born workers. Today’s post taps into unemployment data for these groups.

The FRED graph above shows US Bureau of Labor Statistics data on the fraction of the native-born labor force (solid blue line) and foreign-born labor force (dashed green line) who are out of a job and actively seeking one.

These data don’t show major differences between the groups, but recent research from Alexander Bick at the St. Louis Fed uncovered nuances in the data. He used the BLS survey that collects household labor market information to examine unemployment rates of immigrants according to how long they had resided in the US.

Between 2014 and 2024, immigrants in the US for more than 3 years often had slightly lower unemployment than native-born workers. More-recent immigrants often had higher unemployment than both those groups.

Average unemployment rates since 2022

• Non-recent immigrants 3.3%
• U.S. natives 3.8%
• Recent immigrants 7.6%

Bick’s analysis also considers the potential effects of undercounting immigrants. If unemployed immigrants are undercounted to a large-enough degree, actual demand for labor may be weaker than what official data show. But he finds the impact to be small: In October 2024, an estimate of unreported recent immigrants would have increased the overall unemployment rate by 0.1 percentage points.

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 “Unemployment Rate – Native Born.” Click on the “Edit Graph” button, select the “Add Line” tab, and search for “Unemployment Rate – Foreign Born.” Don’t forget to click “Add data series.”

Suggested by Diego Mendez-Carbajo.

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.