Federal Reserve Economic Data

The FRED Blog has discussed recent research from Yu-Ting Chiang and Mick Dueholm at the St. Louis Fed about how household liability-to-income ratios changed between 1995 and 2019. Today we showcase another piece of their research that answers the following question: Does the age of the head of the household impact the value of their liabilities relative to their assets?

The short answer is “yes.” Older households hold substantially more assets than liabilities than younger households because they have had more time to pay off debts and accrue savings.

The longer answer is also “yes,” but with a caveat. Read on to learn more about it.

The FRED graph above shows data from the Board of Governors of the Federal Reserve System on the total value of assets and liabilities held by all US households between 1998 and 2022. The value of the liabilities has been divided by the value of the assets to observe their relative growth more easily. That liabilities-to-assets ratio peaked in 2009.

The work by the two St. Louis Fed researchers finds that, between 1953 and 2019, both older and younger households experienced faster growth in liabilities than in assets, but they did so at varying rates. The liabilities-­to-assets ratio of the young grew 21 percentage points (from 0.41 to 0.62) and the ratio of the old grew 5 percentage points (from 0.13 to 0.18). These changes are possibly driven by the increase in life expectancy and the aging of the population since the 1950s.

For more about this and other research, visit the website of the Research Division of the Federal Reserve Bank of St. Louis, which offers an array of economic analysis and expertise provided by our staff.

How this graph wase created: Search FRED for “Households; Total Liabilities, Level.” Next, click the “Edit Graph” button and use the “Add Line” tab to search for and add “Households; Total Assets, Level.” Last, type the formula a/b and click on “Apply.”

Suggested by Diego Mendez-Carbajo.

In recent decades, the US has grown increasingly dependent on imports from China to access a vast variety of goods. The FRED graph above shows Chinese import data: From 1990 through 2016, as China became a globally integrated economy, the US import share from China grew steadily, from close to 2% of aggregate US imports in the late 1980s to close to 22% in 2016.

In recent years, however, policies have been enacted to reduce this dependence on China, as illustrated by the trade war during the Trump administration and the CHIPS and Science Act of 2022. Indeed, the US import share from China has declined from 22% to 14% since 2016.

As the cost of importing Chinese goods has increased, the incentive to produce goods domestically has also increased. So, to what extent is the US-China decoupling leading to a resurgence of American manufacturing? We investigate this question in the FRED graph below, plotting manufacturing investment in structure and equipment, as well as employment and output.

On the one hand, there has been a resurgence of manufacturing investment in structures since 2020. These investments may indicate that American manufacturing overall is indeed resurging, with investments in structures more than doubling in a short period.

On the other hand, output, employment, and investments in equipment haven’t increased in tandem with the growth of investment in structures. We interpret these findings as evidence that American manufacturing may be resurging, but that the resurgence may take time: Investment in structures is time-intensive and precedes the growth of employment and output that results once new manufacturing plants are completed.

How these graphs were created: First graph: Search FRED for and select “U.S. Imports of Goods by Customs Basis from China.” From the “Edit Graph” panel, use “Edit Line 1” to add “U.S. Imports of Goods by Customs Basis from World” to the existing series. Under “Customize data,” type a/b into the formula bar, and click “Apply.” Set “Modify Frequency” to “Annual.”
Second graph: Search FRED for and select “Real private fixed investment: Nonresidential: Structures: Manufacturing/Real Gross Domestic Product.” From the “Edit Graph” panel, use “Add Line” to add “Real Gross Private Domestic Investment: Fixed Investment: Nonresidential: Equipment,” “Manufacturing Sector: Real Sectoral Output for All Workers,” and “All Employees, Manufacturing.” Under the “Edit Line” tab for each of the four lines, change the “Units” to “Index (Scale value to 100 for chosen date)” and enter “2010-01-01” for the base period.

Suggested by Jason Dunn and Fernando Leibovici.

March 14, also written as 3/14, is widely celebrated as π day. This is taken seriously in St. Louis, which is home to the 314 telephone area code.

The FRED Blog team is always eager to celebrate the occasion, so today we offer some pie charts. Now, using pie charts is rarely a good idea. They’re not as informative as other chart types. But today we make an exception and use three of them to track different groups’ relationships with bakery products—including pie, of course!

The chart above shows how much individuals from six age categories spend on bakery products. Imagine the United States as a large family with one member from each age group. If each were spending the same amount on bakery products, we would see a pie with six equal slices. But we do not see that. It seems that, as age increases, so does pie consumption. An advantage to becoming older, perhaps?

The second pie chart shows a different family in which each member belongs to a different income category. Not surprisingly, the poorer cousin spends less on bakery products than the wealthier grandparent. Qu’ils mangent de la brioche!

And our last pie shows the four different Census regions. Here, the differences are more subtle. It looks like bakery products are more appreciated in the Northeast and the West. Or they’re simply more expensive and require greater expenditures. Older data on the price of bread by region seem inconclusive. And, although there shall be enthusiastic pie eating in St. Louis today, we don’t expect that will move the annual Midwest statistic.

How these charts were created: For each, the principle is the same: Search FRED for the first series with, say, “expenditures bakery by age” and take one result. From the “Edit Graph” panel, use the “Add Line” tab to search for and select the next series. Repeat until you have a complete set. Use the “Format” tab to choose graph type “pie.” Bon appétit.

Suggested by Christian Zimmermann.