Federal Reserve Economic Data

We have a good idea about the number of new businesses being created because an essential part of that process is to apply for an EIN (Employer Identification Number) for tax purposes. The Census Bureau uses this information to publish weekly data on business applications. The FRED Blog used this data pre-pandemic to map new businesses by state.

The FRED graph above shows business applications over the past 15 years, and two aspects of the data are striking:

1. The regularity of seasonal variations through each year is impressive. In fact, one could confuse the graph with a cardiogram of a steady heartbeat.
2. Business formation becomes febrile after the pandemic-related recession. This erratic behavior is natural as an economy recovers, shedding weak businesses during the recession and regaining new ones in the recovery.

This second phenomenon may be stronger than usual this time around, as it appears that there’s some reorganization happening in the labor market. But the data are too recent to allow us to compare magnitudes across recessions at this point.

How this graph was created: Search FRED for “Business applications,” click on the link, and you’re in business.

Suggested by Christian Zimmermann.

The FRED Blog has tapped into Consumer Expenditures Survey (CES) data before, to track reading and smoking across different groups of consumers. Today, Dia de los Muertos, we use CES data to discuss life…and other insurance purchases.

The FRED graph above shows consumer expenditures on life insurance and other personal insurance for three age groups:

• mid-20s to mid-30s in green
• mid-40s to mid-50s in orange
• mid-60s to mid-70s in red.

We omit the in-between age groups to keep the graph uncluttered and emphasize our main points: Insurance purchases increase with age, and the oldest consumers are buying larger and larger amounts.

Income increases with age, so the fact that spending also increases with age is not surprising. This summary of the Survey of Consumer Finances shows that average household income is lowest when the reference person is 35 years or younger. The same survey also shows that income peaks between ages 45 and 54, so the increased insurance purchases by even older consumers is not exclusively driven by income.

The data above exclude health insurance premiums and illustrate a change in spending patterns brought about by a particular population cohort: those 57 years of age and older. These boomers were born within the time span of 1946 and 1964 and the youngest of them turned 57 this year. Over the past 20 years, they have spent, on average, as much on life and other personal insurance as consumers a decade younger.

And speaking of age: FRED turned 30 this April and is a bona fide millennial. FRED will surely be spending more time and attention on life and other personal insurance data in years to come.

How this graph was created: Search for and select “Expenditures: Life and Other Personal Insurance by Age: from Age 25 to 34.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Expenditures: Life and Other Personal Insurance by Age: from Age 45 to 54” and “Expenditures: Life and Other Personal Insurance by Age: from Age 65 to 74.” To change the line colors, use the choices in the “Format” tab.

Suggested by Diego Mendez-Carbajo.

Part of the “My favorite FRED graph” guest post series.

The FRED graph above shows gasoline prices and real personal consumption expenditures on motor vehicle fuels, lubricants, and fluids between 2006 and 2015.

I use this graph when illustrating how changes in prices influence the quantity demanded of a good or service. The inverse relationship between these two variables is a foundational concept in introductory economics courses. This relationship is also used in econ courses to calculate the price elasticity of demand.

But instead of using made-up figures or imaginary scenarios to illustrate the price elasticity of demand, I direct my students to use real data from FRED. My co-author and I published an article in Journal of Economic Education presenting a step-by-step process for instructors to use this type of FRED data in their classrooms: My students download the data behind this graph or hover over it to write down price and quantity index values; then, they use conventional formulas to calculate consumers’ responsiveness to price changes between any two dates.

And this exercise has a bonus teachable moment. In a couple of occasions, the elasticity of demand coefficients are positive, thus providing an opportunity to discuss the “non-price determinants” of demand. For example, the positive elasticity coefficient between 2008 and 2009 provides an opportunity to discuss the Great Recession and how it may have affected real personal consumption expenditures on motor vehicle fuels, lubricants, and fluids. Data like these help me bring economics to life.

A note about the data in the graph: This line graph shows two data series from two different sources: The blue line shows conventional gasoline prices, measured in dollars per gallon, as reported by the Energy Information Administration. The red line shows the quantity of real personal consumption expenditures on motor vehicle fuels, lubricants, and fluids, measured using an index, as reported by the U.S. Bureau of Economic Analysis.

How this graph was created: Search for and select “Conventional Gasoline Prices: U.S. Gulf Coast, Regular.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Real personal consumption expenditures: Nondurable goods: Motor vehicle fuels, lubricants, and fluids (chain-type quantity index).” To change the date range, adjust the start and end dates above the graph.

Suggested by Carlos J. Asarta.