Federal Reserve Economic Data

When we talk about the labor market we often focus on the unemployment rate. But an equally important measure of labor market conditions is the labor force participation rate (LFPR).

The LFPR is equal to the employed plus the unemployed, divided by a measure of U.S. population. Think of it as those who want to work (i.e., have a job or want one) relative to those who could work (the entire population over age 16 that isn’t incarcerated or on active military duty).

The above FRED graph plots the monthly U.S. LFPR starting in 1948. One striking feature is its hump shape, which is related to demographic factors. LFPR fluctuates around 59% until the late 1960s, when it starts rising. This rise is attributed to the Baby Boomer generation joining the labor force, as well as to the widespread entry of women into the labor force. The LFPR plateaus at around 67% in the late 1990s and starts to decline in the 2000s.

This decline wasn’t just a result of the Financial Crisis of 2007-08. The year 2008 is also when the oldest Baby Boomers started turning 62, the earliest age they could claim Social Security benefits.

LFPR has declined since then, which can be explained by the Baby Boomers retiring and slower U.S. population growth: Subsequent generations have been smaller than the Baby Boomer generation, so their entry into the labor force hasn’t made up for the retiring Boomers.

The recent COVID-19 crisis led to the largest drop in LFPR on record: from 63.4% in February 2020 to 60.2% in April 2020. (See this Economic Synopses essay for more on retirement during COVID-19.) While the LFPR has partly recovered, it is still below its pre-COVID levels. However, the trend line in the graph shows that the post-pandemic LFPR is not far from what we would expect, given its downward trend.

How this graph was created: Search for and select “Labor force participation rate.” From the “Edit Graph” panel, use the “Add Line” tab to select “Create user-defined line.” Set the start and end dates to January 2000 and January 2022, respectively, and the start and end values to the predicted labor force participation rates for each date based on the regression—here, 67.38711 and 61.56833, respectively. A note about the trend line: FRED has no built-in trend line functionality, so we had to download the data (Excel, Stata, or other statistical packages work), regress date on labor force participation rate values from January 2000 to January 2022, and then calculate the predicted labor force participation rates for those dates.

Suggested by Miguel Faria e Castro and Devin Werner.

The Russian Federation is the largest country on earth by area, but it is smaller than the European Union both economically and demographically. Since 2013, the EU has included 27 member countries. In 1989, typically seen as the end of the Cold War, it included 11 member countries. But neither the discussion below nor the data above depend critically on the date you choose to start comparing Russia and the EU.

GDP

The blue line shows the ratio (expressed in percentages) of Russia’s GDP to the EU’s GDP. From 1989 through 2020, Russian GDP never exceeded 15% of the EU’s. The peak occurred in 2012. Since 2013, Russian GDP has grown more slowly than the EU’s—hence, its decreasing relative size.

To gauge the importance of this difference, consider the following thought experiment: Suppose the EU spends 4% of its GDP on defense (4% of GDP is approximately how much the U.S. spends on defense). Because Russian GDP was approximately 10% of EU GDP in 2020, Russia would have to spend 40% of its GDP on defense to simply match EU spending. Obviously, 4% is just an example, but it illustrates the principle that Russia must spend 10 times more of its GDP to match the EU.

Population

The red line shows that Russia’s population has decreased from about 35.2% to 32.2% of the EU’s population from 1989 to 2020.

Comment

Combined together, the GDP ratio and the population ratio imply that the Russian Federation’s GDP per capita is also smaller than the European Union’s: about two-third smaller in 2020. This post doesn’t attempt to answer the difficult question of whether the outcome of a conflict is determined by total GDP or GDP per capita. Certainly, both variables are likely to play a role, among others. But in its current confrontation with the European Union, the Russian Federation is likely to be at a disadvantage on these two fronts.

How this graph was created: Search FRED for “GDP Russia.” From the “Edit Graph” panel, search for “GDP European Union” and apply formula a/b*100. Open the tab “new line” and repeat with population.

Suggested by Guillaume Vandenbroucke.

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.