Federal Reserve Economic Data

The FRED graph above plots the consumer price index for motor vehicles: Clearly, the prices of both new and used cars have risen over the course of the pandemic. What’s been driving this rise in prices?

On the one hand, the generous fiscal stimulus to mitigate the impact of the pandemic likely increased demand and put upward pressure on car prices. On the other hand, as the graph also shows, total domestic auto production has been and remains substantially below January 2020 levels, which suggests that supply-side factors have also likely played a role in the rise of car prices. With the demand for cars outpacing production during this period, car inventories were gradually depleted from early 2020 to the present, as also shown in the graph.

The outbreak of COVID-19 and its impact on workplace operations may account for the drop in production over the first few months of the pandemic, but it’s unlikely that these factors account for the persistent decline of domestic car production.

A popular narrative attributes this decline to semiconductor shortages. Semiconductor production indeed experienced significant pressure over the past couple of years, with demand spiking and manufacturers unable to scale up production fast enough to meet that demand. Because semiconductors have become a critical input for the production of cars, an input with few close substitutes, shortages of semiconductors can severely curb the production of new cars.

So, semiconductor shortages are likely playing a significant role. But the FRED graph below shows that the recent decline of car production falls broadly within the long-term downward trend over recent decades: Car production has generally been declining since the mid 1990s. This trend suggests that, even without the significant increase in demand following COVID-19, car production may still have continued to decline; moreover, car production may not revert to pre-pandemic levels even after the semiconductor shortages end and demand eases up.

How these graphs were created: Search FRED for “Consumer Price Index for All Urban Consumers: New and Used Motor Vehicles in U.S. City Average.” From the graph, click on “Edit Graph,” open the “Add Line” tab, and search for and select “Domestic Auto Production” and “Domestic Auto Inventories” Use the “Edit Lines” tab to change units to “Index (Scale value to 100 for chosen date).” Enter the base year date as “2020-01-01.” Select the “Copy to All” button to apply to all series. Set the earliest date in the window to “2020-01-01.” For the second graph, search FRED for “Domestic Auto Production.”

Suggested by Jason Dunn and Fernando Leibovici.

Next Monday is a holiday for the St. Louis Fed: since 1937, Columbus Day, and since last year’s presidential proclamation, Indigenous Peoples’ Day. This post, like so many others, offers timely examples of the data you can find in FRED—today, it’s specific to places in the U.S. bearing the name Columbus.

For these 3 metropolitan statistical areas and 1 county, we focus on the unemployment rate because it’s a good gauge of the health of a region and has units that don’t depend on the region’s size. And, as it turns out, these four regions are quite different from each other:

• Columbus, Ohio, is a major city with substantial public employment, led by Ohio’s state government and The Ohio State University.
• Columbus, Indiana, concentrates more on manufacturing.
• Columbus, Georgia (plus a county in Alabama), is remarkable for its military focus.
• Columbus County, North Carolina, is rural with a declining population.

The FRED graph above shows that the unemployment rate in these four regions varies quite a bit over time but, remarkably, consistently maintains the ranking of these four regions. (The brief exception is when the rate in Columbus, Indiana, shot up at the height of the pandemic: The many factories there didn’t have a work-from-home option and closed temporarily.)

How this graph was created: Search FRED for “Columbus unemployment” and select one of the four, making sure not to take the smoothed series. From the “Edit Graph” panel, open the “Add Line” tab and repeat the search until the graph is complete.

Suggested by Christian Zimmermann.

Since 2019, the Board of Governors of the Federal Reserve System has combined data from two different surveys to provide quarterly estimates of how wealth is distributed among U.S. households. The Distributional Financial Accounts (DFA) provide these data as far back as 1989 and help describe patterns in wealth concentration in the United States.

FRED recently added several series from this dataset, which allows us to identify wealth holdings of segments of the population, from the top 0.1% through the bottom 50%.

The areas in the FRED graph above show the total wealth held by five different groups of households:

• the wealthiest 0.1% (in blue)
• the next 0.9% (in red)
• the next 9% (in purple)
• the next 40% (in cyan)
• the bottom 50% (in orange)

Keep in mind there are large differences in the number of households in each of these five groups.

What does it take to be counted among the wealthiest households? Well, FRED also has data on the minimum thresholds for each segment. At the time of this writing, each of the wealthiest 0.1% of households had at least $38 million worth of assets. In total, those 130,757 households in the top segment held almost twice as much wealth as the 65 million households in the bottom 50%.

Check back soon. The FRED Blog will continue to use DFA data to describe the current distribution of wealth in the U.S. and its changes over time.

How this graph was created: In FRED, navigate the list of releases to “Distributional Financial Accounts.” Click the boxes next to the five categories of total assets held by wealth percentile groups and click “Add to Graph.” Use the “Format” tab in the “Edit Graph” panel to change the graph type to “Pie chart.”

Suggested by Diego Mendez-Carbajo.