Federal Reserve Economic Data

The fourth quarter of the year, which includes the Christmas shopping season, is the busiest for retailers. Hark! As expected, it’s also the time of the year when credit card holders make the most intensive use of their access to credit.

The FRED graph above tracks credit card use from a dataset provided by the Federal Reserve Bank of Philadelphia. The utilization rate shown in the graph is the percent of the total available credit line that a borrower is using at the end of a billing cycle. The data are available since the third quarter of 2012 and aren’t seasonally adjusted. The telltale see-saw pattern in the plotted data reveals the timing of the most-intensive use of credit cards: the fourth quarter of the year.

This seasonal pattern exists across the three different groups of credit card holders reported in the data release:

• Green: This typical group, in the 50th percentile, uses an average of about 9% of their available credit limit.
• Red: This more-intensive group, in the top 25th percentile, uses an average of 56% of their available credit limit.
• Blue: This most-intensive group, in the top 10th percentile, nearly max out their available credit limit, at an average of 93%.

One more thing: There was a marked decline in credit card utilization rates during the COVID-19 pandemic. A closer look at the data in FRED shows credit card holders aren’t yet racking up their credit card debt with the same intensity as they did prior to 2020. The spike in personal saving during the pandemic described in this FRED Blog post could explain this decreased reliance on charging.

How this graph was created: Search FRED for “Large Bank Consumer Credit Card Balances: Utilization: Active Accounts Only: 50th Percentile.” Click the “Edit Graph” button and use the “Add Line” tab to add the other two series.

Suggested by Diego Mendez-Carbajo.

The FRED Blog has made deep dives into food prices before, bringing a bounty of data treasures to the surface: For example, we’ve shown trends, seasonal patterns, and the impact of distinct economic shocks on the cost of assembling a nutritious meal. Today, we use more food-related data to see what different regions spend specifically on fish and seafood.

The FRED graph above shows the dollar amounts spent on fish and seafood by consumers in each of the four Census regions. The U.S. Bureau of Labor Statistics collects these data for its Consumer Expenditure Surveys. Let’s dive in:

• Consumers in the Northeast and West Census regions consistently spend the most money of all regions in buying the fruits of the sea.
• This FRED graph shows that, as a fraction of total food expenditures, consumers in the South Census region are not far behind the other two coastal regions.
• Consumers in the Midwest, perhaps because they’re largely landlocked, spend the least amount on fish and seafood, both in dollars and as a fraction of their overall food expenditures.

FRED doesn’t currently have regional prices on food items, so we can’t say whether Midwest consumers shy away from a relatively pricier type of meal or if their relatively smaller spending on seafood is strictly a matter of taste.

Either way, keep a line in the water of the FRED Blog. We’ll keep swimming among our data series to try to haul in a good catch of insights for you.

How this graph was created: First, cast your net in FRED by searching for “Expenditures: Fish and Seafood by Region: Residence in the Northeast Census Region.” Select the series and click “Edit Graph” at the top right corner. Then navigate to the “Add Line” tab, where you can search for the three other regional Census data series; select each by clicking “Add data series.”

Suggested by Diego Mendez-Carbajo.

The FRED Blog has discussed the large differences in wealth between the richest 0.1% and the poorest 50% in the U.S. Today, we try to answer a related question by exploring a different data set from the Distributional Financial Accounts (DFA): What does it take for households to move up on the wealth ladder?

The FRED graph above shows the minimum dollar values of wealth that households must have to be classified in each DFA wealth category. As of 2019,

• to be in the top 0.1% (blue diamonds), a household needs at least $38 million
• for the next 0.9% (red diamonds), the threshold is a little over $10 million
• for the next 9% (green diamonds), the threshold is almost $1.8 million
• for the next 40% (purple diamonds), the threshold is $165,382
• and, obviously, the minimum for the bottom 50% is $0

These minimum wealth cutoff values are reported once every three years and increase in value due to inflation and changes in the distribution of wealth. To make that last point clearer, we created a second FRED graph showing the distance between the wealth brackets (i.e., ratios of wealth cutoff values). To make our references clearer, let’s label the brackets as follows: 1st bracket (top 0.1%), 2nd bracket (99% to 99.9%), 3rd bracket (90% to 99%), and 4th bracket (50% to 90%).

Again, the latest data at the time of this writing are for 2019. The blue circles show the distance between the 1st and 2nd brackets: 1st bracket households need at least four times more wealth than the households at the bottom of the 2nd bracket. The green circles show the distance between the 3rd and 4th brackets: 3rd bracket households need nearly 11 times more wealth than the households at the bottom of the 4th bracket.

Some takeaways: It’s harder, relatively speaking, to move from the bottom 50% into the top 10% than it is to move from the top 10% into the top 1% or even the top 0.1%. Perhaps that’s expected. But it also may be useful to know the threshold to get into the wealthiest 10% has risen noticeably faster than the thresholds for the other brackets. That is, the gap between the very wealthy and the middle-of-the-road wealthy is growing over time: Climbing that next rung of the wealth ladder is becoming gradually harder.

How these graphs were created: For the first graph, search FRED for “Minimum Wealth Cutoff for the Top 0.1% (99.9th to 100th Wealth Percentiles).” Next, click “Edit Graph” at the top right corner and navigate to the “Add Line” tab. Search for the homonymous data series for the 99th to 99.9th wealth percentiles and click on “Add data series.” Repeat the last step to add data for the remaining two wealth groups. For the second graph, start with a graph of the “Minimum Wealth Cutoff for the Top 0.1% (99.9th to 100th Wealth Percentiles)” data series. Next, click “Edit Graph” and customize the data in Line 1 by searching for “Minimum Wealth Cutoff for the 99th to 99.9th Wealth Percentiles” and clicking on “Add.” Next, type the formula a/b and click on “Apply.” Repeat the last two steps to calculate the ratios between minimum wealth cutoffs for the remaining two wealth groups.

Suggested by Diego Mendez-Carbajo.