Federal Reserve Economic Data

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.

The FRED Blog has described how the value of technology exports from the United States to the rest of the world has steadily declined. This post adds geopolitical characteristics to the discussion.

The FRED graph above uses Bureau of Economic Analysis data to track the inflation-adjusted value of US technology services exported to other countries, measured in royalties and licensing fees. The data are presented as a fraction of the total value of exported services to more easily observe their change over time.

Between 2007 and 2019, the relative value of US technology exports shrank. After a short-lived reversal in 2020-2022, likely related to economic disruptions from the COVID-19 pandemic, that value continued to decline.

Recent research from the Federal Reserve Bank of St. Louis could help explain at least part of this trend.  Ana Maria Santacreu and Samuel Moore use country-specific trade data and a measure of foreign policy preferences to discuss how countries’ geopolitical characteristics affect their purchase of US technology services:

Countries that don’t share the same foreign policy priorities and values espoused by the US are less likely to have robust international trade relations with the US than countries that do share these priorities and values. So, contentious political relations and shifting strategic alliances do erode the economic ties that bind countries. Although geopolitical tensions don’t drive the overall relative decline of US technology exports, they do offer a relevant context for telling the stories behind the numbers.

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 “Real exports of services: Royalties and license fees.” Click on the “Edit Graph” button and select the “Edit Line” tab to customize the data by searching for “Real exports of services.” Don’t forget to click “Add.” Next, type the formula (a/b)*100 and click “Apply.”

Suggested by Diego Mendez-Carbajo.