Federal Reserve Economic Data

Over the past few years, job quits has been one of the more closely watched labor market metrics. But not everyone has been monitoring the same exact data series. FRED has two versions of quits: one seasonally adjusted, one not seasonally adjusted. Both measure the number of times workers left their job (excluding retirements), but different audiences may find one version more suitable to their needs.

The unadjusted series shows how late fall into winter seems to be a particularly unpopular time to leave one’s job, while activity picks up during the summer. Consider how a desire to receive a holiday bonus might prevent people from leaving their jobs during November and December, how students heading back to school can lead to a wave of turnover in August, or how the flip to a new year could spur some workers to want a fresh start at a new job in January.

For some users of FRED, these data can provide valuable insights to inform action planning. If company leaders know that the winter likely won’t see much change, they can use that time to prepare for the potential attrition at other points in the year. Or a savvy employee could leverage these data to negotiate a retention bonus, threatening to join the summer exodus if their employment needs aren’t met.

But for other users, the unadjusted data series is unnecessarily messy. The frequent movement from month to month comes at the expense of being able to clearly see longer-term trends. The seasonally adjusted series removes the impact of factors specific to certain times of the year, resulting in a much smoother line.

For economists and policymakers, the adjusted data make it much easier to analyze how underlying factors impact worker movement. When the seasonally adjusted number of quits changes over an extended period, it signals that economic conditions, policies, and other factors have led to changes in worker behavior. This perspective is behind past FRED blog posts (such as here, here, and here) that have reported on seasonally adjusted quits.

One series isn’t better than the other; but, depending on what you’re analyzing, one may be a better fit for your specific purpose.

How this graph was created: Search FRED for “quits” and click on “Quits: Total Nonfarm” to create a graph with the seasonally adjusted version of the data. Then, from the “Edit Graph” panel, use the “Add Line” tab to search for and select the non-seasonally adjusted version of the same name (you may find it easiest to search using the series code “JTUQUL”).

Suggested by Andrew Spewak.

The FRED Blog is proud to have reached the milestone of 900 blog posts. As with every centennial, we present a graph that’s related to the number. Although 900 was challenging, FRED always delivers.

Today’s topic is gasoline, and the data set comes from a survey of 900 retailers. The values reflect the average prices of “regular” gasoline, with octane levels of 85 to 88. The FRED graph above offers some not-so-surprising observations: Gas prices fluctuate dramatically, and gas prices have increased substantially since the 1990s, peaking in mid 2022.

Adjusting for consumer price inflation, as we do in our second graph, shows the same variability but reveals something new: After the increase in the early 2000s, the real gas price has not been trending up and the 2022 peak in the first graph is surpassed on several occasions. But, of course, what really matters is how gas prices relate to incomes, which we show in our third graph. There, we see that current gas prices (measured as the number of minutes of work it takes to purchase a gallon of gasoline) are not that much higher than in the 1990s.

How these graphs were created: The first graph can easily be found by searching FRED for “gasoline price.” For the second, click on “Edit Graph,” in the “Add Line” tab search for CPI, and then apply formula a/b*100. For the third, replace the CPI by the average hourly wage (selecting the series for non-supervisory workers, which goes back further than other series) and then apply formula a/b*60.

Suggested by Yvetta Fortova and Christian Zimmermann.

What do conditions look like for business owners? It’s a complicated question. On the pessimistic side, inflation remains elevated and supply chains are still working their way back to pre-pandemic levels (though both have seen improvement in recent months). Labor markets are historically tight, and interest rate increases over the previous year have raised borrowing costs. Add that to broader uncertainty about the near-term economic outlook, and it’s easy to see why the NFIB’s Small Business Optimism Index has been below its 49-year average for 17 consecutive months.

Yet more people than ever are starting businesses in this environment, according to the Census Bureau’s Business Formation Statistics. Tracked since 2006, these data show the number of applications for an Employer Identification Number (EIN), which is a unique federal tax identifier linked to wage-paying businesses. (Sole proprietorships with no employees often don’t have an EIN.) In this dataset, the Census Bureau categorizes some business applications as “high propensity,” which have a high rate of leading to a business with a payroll. Manufacturing, retail, healthcare, and food service all fall under this category.

Applications for all businesses rose from a seasonally adjusted average of 200k per month to just over 300k per month during the 2010s. For the past two years, they’ve held steady at over 400k per month. High-propensity applications have seen a similar increase: After staying below 100k per month for the 2010s, they’ve been above 130k for the past two years.

It’s likely these increases reflect shifting economic conditions in the wake of the COVID-19 pandemic, and additional research could help us better understand how and why they’re occurring. For now, it’s enough to know that some business owners feel that conditions are tough, while others are taking this moment to launch new businesses.

How this graph was created: Search FRED for “Business Applications: Total for all NAICS.” Use the “Add Line” option to and search for and select “High-Propensity Business Applications: Total for all NAICS.”

Suggested by Nathan Jefferson.