Federal Reserve Economic Data

The FRED Blog has discussed how the COVID-19 pandemic changed the sale volumes of different products, from groceries and alcohol to men’s clothing, sporting goods, pharmacies and drug stores. The social distancing required to manage the pandemic also impacted how people shopped, boosting online sales. Today, we compare nonstore and e-commerce retail sales to total sales to see if the boost to online sales was permanent or temporary.

The FRED graph above shows data from the U.S. Census about where consumers do their shopping. The blue line compares monthly nonstore retail sales (i.e., home delivery, TV or print catalog sales, and electronic shopping) with all other non-food, non-motor-vehicle retail sales. The red line compares quarterly retail sales over the internet with total retail sales. All data series are seasonally adjusted and presented as percent rates, or proportions.

The parallel rising trends of these data indicate that shopping over the internet and away from stores is gradually growing in popularity. The spike in distance shopping during the early months of the pandemic is very noticeable. However, the gradual decline afterward strongly suggests nonstore and e-commerce retail sales are back to trend and that the pandemic-related boost was temporary.

Although FRED doesn’t currently have any data to compare brick-and-mortar window shopping to internet browser window shopping, perusing “the shelves” in IDEAS yields multiple research papers on the topic. We invite you to try these on for size.

How this graph was created: Search for and select “Advance Retail Sales: Nonstore Retailers.” From the “Edit Graph” panel, use the “Add Line” tab to search for “Advance Retail Sales: Nonstore Retailers.” Remember to click “Add data series.” Next, use the “Edit Lines” tab to customize the data in line 2 by searching for and selecting: “Advance Retail Sales: Retail Trade and Food Services, Excluding Motor Vehicle and Parts Dealers,” “Advance Retail Sales: Food Services and Drinking Places,” and “Advance Retail Sales: Food and Beverage Stores.” Next, create a custom formula to combine the series by typing in (a/(b-c-d))*100 and clicking “Apply.”

Suggested by Diego Mendez-Carbajo.

The economic effects of COVID-19 and the subsequent recovery have been markedly different across countries. So it’s a good thing FRED has international data. Here, we compare the recent dynamics of output, demand, and inflation for three developed economies: Canada, Germany, and the United States.

Output: GDP

Economic activity contracted sharply in all three countries with the onset of COVID-19 in early 2020. The contraction was mildest in the U.S. and most severe in Canada. But the economic differences across these countries become wider throughout the recovery. The U.S. recovered the fastest, with real GDP in 2021:Q4 about 5% higher than in 2020:Q1. The recoveries in Canada and Germany have been slower: Real GDP in 2021:Q4 increased about 2.5% and 0%, respectively, relative to 2020:Q1.

Demand: Imports

These differences in output are paralleled by the differences in demand. We use real imports of goods and services in the FRED graph above as a proxy for aggregate demand. The U.S. experienced the mildest contraction and the sharpest recovery: Import levels in the U.S. had surpassed their pre-pandemic levels by 2020:Q4 and currently stand close to 15% higher than pre-pandemic levels. Imports in Canada and Germany were still below their pre-pandemic levels in 2021:Q2 and 2021:Q3, respectively.

Inflation: CPI

Not surprisingly, these differences in output and demand are paralleled by the differences in inflation. During the recession itself, consumer price index (CPI) levels in all three countries remained fairly stagnant relative to 2020:Q1. They rose gradually and in tandem until about May 2021. U.S. CPI has accelerated its relative rate of increase since June 2021, potentially as a result of its relatively larger increase in demand.

The differences in these dynamics point to the different effects COVID-19 has had on these economies. Look for a deeper comparative cross-country analysis in future posts, where we hope to disentangle the relative importance of the various forces at play.

How these graphs were created: First graph: Search and select “Real Gross Domestic Product for Germany.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Real Gross Domestic Product” and “Gross Domestic Product by Expenditure in Constant Prices: Total Gross Domestic Product for Canada.” 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.” Click the “Copy to all” button to apply to all series.
Second graph: Use the same steps above to graph “Real Imports of Goods and Services for Germany,” “Real Imports of Goods and Services,” and “Real Imports of Goods and Services for Canada.”
Third graph: Use the same steps above to graph “Consumer Price Index of All Items in Germany,” “Consumer Price Index for All Urban Consumers: All Items in U.S. City Average,” and “Consumer Price Index: Total, All Items for Canada.”

Suggested by Jason Dunn and Fernando Leibovici.

The FRED Blog has used data compiled by Daniel Aaronson, Daniel Hartley, and Bhashkar Mazumder to show the lasting effects on U.S. home values of the color-coded maps created by the Home Owners’ Loan Corporation in the 1930s. Today, we examine the data on rent and rental costs included in that data set.

The FRED graph above shows the same type of rental cost layering we described for home values: Between 1930 and 2000, redlined neighborhoods (areas with a “D” letter code) consistently recorded the lowest rent prices. That is to be expected, as properties in the area were old or nearby unattractive or unhealthy industrial areas. However, relative to the typical home values in those redlined areas, rental costs were consistently high during much of this period.

The second FRED graph shows the rent and rental costs in each HOLC color-coded neighborhood as a fraction of the median, or typical, home value in the same area. To leave more room for the data, the graph doesn’t display the legend (graph with legends), but the line colors match those used in the first graph. Both rental costs and home values are adjusted for inflation. Their ratio, or proportion, show a broadly declining trend between 1940 and 2000: Home values were generally growing faster than rental costs.

In conclusion, while homeowners in redlined and yellowlined neighborhoods were hindered from building up wealth by the combination of higher-than-average financing costs and lowest-of-all home values, home renters in those same neighborhoods faced proportionally higher rent costs than almost all other residents between 1940 and 1990.

How these graphs were created:
Rent and Rental Costs. From FRED’s main page, browse data by “Release,” search for “The Effects of the 1930s HOLC ‘Redlining’ Maps,” and select “Summary Statistics.” Under “Panel D. Rent” check the box to the left of each of the four HOLC neighborhood categories. Next, click on the “Add to Graph” button. Lastly, from the “Edit Graph” panel, select the “Format” tab to match the color of each line to their HOLC designation and to turn off the “Recession shading.”
Rent-to-Home-Values Ratios. Edit the graph of rent and rental costs by selecting the “Edit Line 1” tab to customize the data by searching for and selecting “Median Home Values in Home Owners Loan Corporation (HOLC) Neighborhood A.” Next, create a custom formula to combine the series by typing in “a/b” and clicking “Apply.” Repeat the same steps for the other three lines in the graph, changing the letter designating the neighborhood to B, C, and D as it corresponds.

Suggested by Diego Mendez-Carbajo.