Federal Reserve Economic Data

The FRED Blog has discussed the four possible reasons for being (and being categorized as) unemployed: From most to least prevalent, they are involuntarily losing a job, voluntarily leaving a job, looking for work after being out of the labor force for a while, and looking for work for the first time.

The FRED graph above shows data from the US Bureau of Labor Statistics breaking down the headline unemployment rate into these four reasons. The data are stacked to easily observe the change in the unemployment rate, and we can see a gradual rising trend between April 2023 and August 2024.

Recent research by Maximiliano A. Dvorkin and Serdar Ozkan at the St. Louis Fed offers new insights into unemployment flows during that period. They find that more-frequent job separations are the main reason behind the rising unemployment rate. Moreover, their research puts this trend in context by comparing inflows and outflows in the labor market during three different recessions: 1990-91, 2001, and 2007-09. They conclude that upticks in the proportion of people losing their jobs have been observed before the start of past recessions and that keeping an eye on labor dynamics may provide an early indication of a change in broad economic conditions.

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 the graph was created: Browse FRED data by Release and navigate to “Employment Situation > Release Tables > Current Population Survey (Household Data) > Table A-11. Unemployed persons by reason for unemployment.” Next, click on the boxes to the left of three categories of “Unemployed as a Percent of the Civilian Labor Force” at the bottom of the page. Last, click on the “Add to Graph” button.

Suggested by Diego Mendez-Carbajo.

The FRED Blog has examined how the internet has affected our choices of where to shop for certain goods and services. And recent research from Paulina Restrepo-Echavarría at the St. Louis Fed has examined how the internet may have affected our choices related to dating and marriage.

New trends in dating and marriage aside, most households are still occupied by married couples, although the fraction of these households dropped from a peak of 87.8% in 1953 to 73.7% in 2023.

The FRED graph above shows data from the US Census Bureau on types of households, which are categorized by the person or persons in whose name the housing unit is rented or owned: married couples in blue, male householders in red, and female householders in green. These annual data are displayed in a stacked area graph to easily compare the relative amounts of these family household types.

Restrepo-Echavarría’s research compares married couples in the 2008-2021 period, as the use of online dating was rising, with those from the pre-internet years of 1960-1980. She and her coauthors find that people have increasingly been marrying someone more like themselves, with the same income, education, and skill levels.

They also look at some of the economic effects of this homogeneity, which they find has contributed to the increase in household income inequality between 1980 and 2020. 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 “Family Households with Married Couples.” Click on the “Edit Graph” button, select the “Add Line” tab, and search for “Family Households with Male Householder.” Don’t forget to click “Add data series.” Repeat the last search step to add “Family Households with Female Householder” to the graph. Last, use the “Format” tab to select “Graph type: Area” and “Stacking: Percent.”

Suggested by Diego Mendez-Carbajo.

What do steam locomotives, gas-powered automobiles, and generative artificial intelligence all have in common? They’re impactful and disruptive technologies that became widely adopted.

In this post, we discuss how quickly these technologies were adopted, with the help of a graph of macrohistory data from the National Bureau of Economic Research and recent research from St. Louis Fed economist Alex Bick and co-authors Adam Blandin and David Deming.

The FRED graph above shows the number of available steam locomotives (solid line) and automobile registrations (dashed line) in the United States between 1889 and 1916. Each data series is plotted on a separate axis and displayed in a logarithmic scale to make their comparison easier.

Both transportation technologies show fast adoption rates. The number of available steam locomotives doubled between 1889 and 1911, a time span of 22 years. During roughly the same time, 1895 to 1917, the number of car registrations grew by a factor of 1 million.

On a completely different scale, the first nationally representative US survey of GenAI adoption at work and at home shows that at least 1 million subscriptions of the first GenAI model were sold in roughly two years.
Comparing the adoption rates of capital-intensive transportation goods such as steam locomotives and automobiles to a service such as computer-assisted text, image, and audio generation has obvious limitations. But, the speed of adoption of all these technologies speaks to their impact on people’s lives and the economy at large.

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 “Steam Locomotives Available for United States.” Click on the “Edit Graph” button, select the “Add Line” tab, and search for “Automobile Registrations, Passenger Cars, Total for United States.” Don’t forget to click “Add data series.” Next, use the “Format” tab to customize Line 2 by selecting “Y-Axis position: Right.” Last, customize the “Display” by selecting both “Log scale left” and “Log scale right” checkboxes.

Suggested by Diego Mendez-Carbajo.