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Federal Reserve Economic Data

The FRED® Blog

Seasonality in food prices: A bountiful harvest of FRED data

The FRED Blog has discussed shocks to meat and fish prices related to the COVID-19 pandemic. Shocks are unexpected changes in the supply or demand of a product or commodity that results in a sudden change in its price. Today, we discuss how the timing of harvesting seasons results in predictable changes in the prices of fresh fruit.

The FRED graph above uses data from the U.S. Bureau of Labor Statistics Consumer Price Index, Average Price Data release: It shows the quarterly dollar prices of a pound of Thompson seedless grapes (green circles) and a dry pint of strawberries (red circles).

When grapes are harvested at the end of the summer (the third quarter of the year) and strawberries are picked in the spring (the second quarter of the year), the abundant supply pushes down their prices to their annual lows. Notice how strawberry prices remain low—or even fall farther—during the third quarter of the year. This report from the Economic Research Service of the U.S. Department of Agriculture reviews these seasonal patterns and highlights the extended growing season for strawberries in the U.S.

For contrast, we also plot the quarterly dollar price of a pound of bananas. Because this fruit grows only in the tropics and tropical weather has little seasonal variation, bananas are picked year-round. There are almost no periodic and/or regular changes to banana prices.

To learn more about fruit price volatility, read this report from the Bureau of Labor Statistics. Like FRED, it is rich in nutrients.

How there graphs were created: For the first graph, search for “Grapes, Thompson Seedless, Per Lb. (453.6 Gm) in U.S. City Average.” Next, use the “Edit Graph” menu’s “Add Line” tab to add the series “Strawberries, Dry Pint, Per 12 Oz. (340.2 Gm) in U.S. City Average.” Next, edit Line 1: Click on “Modify frequency” and select “Quarterly.” Click on “Copy to All” to apply the same change to Line 2. Next, use the “Format” tabe and select “Mark type: Circle.” Last, select colors to taste. For the second graph, search for “Bananas, Per Lb. (453.6 Gm) in U.S. City Average” and repeat the above steps for unit frequency and graph format.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: APU0000711211, APU0000711415, APU0000711417

Retail sales in a pandemic recession

Diverse tales by sectors

The FRED graph above shows retail sales for the last year and a half. Of course, the pandemic has had a huge impact, with a severe drop and a quick recovery. But the retail sector is large and diverse. So let’s look at various layers of it.

This graph is one of the strangest looking ones we’ve ever shown on this blog. And it tells very different stories. Let’s go through them one by one. (Hover over the legend in the graph to better see the respective lines.)

Grocery store sales actually surged with the pandemic. This is likely linked to the substitution from eating out to eating at home, which we discussed earlier on this blog.
Alcohol sales increased as well, as discussed in another post.
Pharmacies and drug stores are also doing well, likely due to a higher demand for prevention goods.
Men’s clothing has been a disaster, bottoming out in April 2020 at 12% of its January 2020 sales (after adjusting for seasonal variations).
Sporting goods sales fell by half but are now higher than before. Whether this rise was caused by simply catching up on postponed sales remains to be seen.
Warehouse clubs and superstores did well initially and are now back to normal.
Online and mail-order shopping… It’s no surprise this area is doing well, but it hasn’t been exploding as much as some may think.

The sectors we highlighted here are special in some ways, but there’s much more to explore from the Monthly Retail Sales release table.

How these graphs were created: For both graphs, start from the release table. For the first graph, simply click on the retail sales excluding food services and restrict the sample to 2019-01-01 to 2020-06-01. For the second graph, check the relevant series, click on “Add to Graph”; from the “Edit Graph” panel, change units to 100 for 2020-01-01, click “Apply to all,” and restrict the sample period to 2019-01-01 to 2020-06-01.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: MRTSSM44000USS, MRTSSM4451USS, MRTSSM4453USS, MRTSSM44611USS, MRTSSM44811USS, MRTSSM451USS, MRTSSM45291USS, MRTSSM4541USS

The high(er) price of health

Our purchases cost more and more over time, given inflation. Tracking the price index for personal consumption expenditures is one way to measure inflation. And the FRED graph above shows that, since 2000, personal consumption expenditures (purple line) have become 40% more expensive. This amounts to an annual rate of inflation of about 1.8%.

Price indexes can be computed for specific spending categories as well—such as food, energy, and health. The Health Expenditures Price index is also shown in this graph (blue line): It’s the way the Bureau of Economic Analysis tracks the price of heath expenditures for households.

The graph reveals how much faster the price of health expenditures is growing relative to the price of general consumption expenditures: It took 19 years for general consumption expenditures to become 40% more expensive, while it took only 7 years for health expenditures to do that. So, the inflation rate for health expenditures is much higher: 3.7% per year.

How this graph was created: On FRED’s main page, search for “Personal Consumption Expenditures”; find and select “Personal Consumption Expenditures: Chain-Type Price Index.” Use the “Edit Graph” menu’s “Add Line” option to search for “Blended” and select “Health Expenditures Price Index, Blended Account Basis.” Click on “Add data series.” In the Units box, choose “Index (Scale value to 100 for chosen date)” and choose the year 2000. Then click “Copy to all.” Return to the graph and restrict the view to 2000-01-01 to 2020-01-01.

Suggested by Guillaume Vandenbroucke.

View on FRED, series used in this post: HLTHSCPIBLEND, PCEPI


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