In times of market turmoil, fear and uncertainty take center stage. One tool analysts use to measure this fear is the VIX, often called the “Fear Index,” published by the Chicago Board Options Exchange (CBOE).

The graph shows that the VIX value is about 20 on average but much higher during periods of extreme uncertainty, such as the onset of the COVID-19 pandemic in 2020 and the financial crisis in 2008:

• In 2020, the end-of-month peak was 53.54 in March and the daily peak was 82.69 March 16.
• In 2008, the end-of-month peak was 59.89 in October and the daily peak was 80.86 November 20.

But what exactly is the VIX?

VIX, or volatility index, is a forward-looking measure of expected future volatility in the stock market. It captures these expectations using prices of “out of the money” (OTM) put and call options on the S&P 500 index. These options are particularly useful in capturing future expectations of extreme price movements. For example, OTM put options help protect against downside risk and become more expensive when investors anticipate a decline in the S&P 500. The VIX calculates a weighted average of implied volatilities across a range of strike prices for these options, providing an estimate of expected volatility over the next 30 days.

The FRED graph shows the VIX’s countercyclical pattern over time: It rises during economic downturns and falls during booms. Why? For example, in recessions, firms borrow more and thus their stock returns could become increasingly volatile. Or investors could become more risk averse and act accordingly. Also, high uncertainty during recessions can potentially further exacerbate these economic downturns.

The VIX, as a barometer of market uncertainty, reflects the collective expectations of investors about future stock market volatility. it is clearly associated with periods of economic turmoil, but it also highlights the natural cycles of confidence and caution in financial markets.

How this graph was created: Search FRED for “VIX” and you’ll have the option to select the “CBOE Volatility Index: VIX” series, with series ID “VIXCLS.”

Suggested by Aakash Kalyani.

At the end of January 2025, FRED posted preliminary job growth data for US metropolitan statistical areas (MSAs) in 2024. These data from the BLS provide a useful glimpse into the differences in job opportunities and broader economic growth across the nation.

US stats

The FRED map above shows the wide range of job growth across 352 MSAs. Median MSA job growth was 1.1%. Most MSAs (220, or 62%) had job growth below the US average of 1.4%, and 41 MSAs had negative job growth (shown in red).

Strongest job growth: 6.5% in Rochester, Minnesota, followed by 5.3% in Stockton-Lodi, California.

Steepest declines: -6.7% in Ocean City, New Jersey, followed by -2.7% in Ithaca, New York.

Eighth Federal Reserve District stats

The median job growth rate of the Eighth Federal Reserve District (the home of FRED) matches the US median of 1.1%.

Strongest job growth: 2.4% in Columbia, Missouri.

Steepest decline: -1.0% in Pine Bluff, Arkansas.

The FRED graph below reports the job growth rates for the four most-populous MSAs in the Eighth District, along with the US average over the past two years. The graph shows that job growth in St. Louis and Little Rock has outpaced the national average over the past two years, while growth in Louisville and Memphis has been slower than the national average.

Of course, these data are subject to revision, as highlighted in this 2017 post. So, this analysis will be revisited in March after the benchmark revision.

How these graphs were created:
Map: Search FRED for and select series ID “STLNA.” Click “Edit Graph” in the upper right: Under “Units,” select “Percent change from year ago.” Click the “View Map” button to see the data across all MSAs. Click “Edit Map”: In the format section’s “Data grouped by” menu, select “User Defined Method” to choose your own data groups and colors.
Graph: Search FRED for and select series ID “PAYEMS.” Click “Edit Graph” then “Add Line”: Search for “STLNA” and click “Add series.” Repeat this for the three other metro areas shown: LRSNA, LOINA, MPHNA. In the “Edit Graph” panel’s “Units” menu, select “Percent change from Year Ago” and click “Copy to all.” Modify the frequency to “Annual” and select aggregation method “End of Period”; repeat this step for each line. From the “Format” section’s “Graph type” menu, select “Bar.” Return to the graph itself and, in the upper right,  modify the date range to “1Y” (1 year).

Suggested by Charles Gascon.

Employer-to-employer (EE) transitions are when workers move from one job to another without being unemployed in between. EE transitions are important for the aggregate economy for several reasons.

• Persons typically change jobs when they’re offered higher salaries, so an economy with a high EE rate may have a higher level of labor earnings and more demand for goods and services.
• EE transitions also facilitate the reallocation of workers across jobs, so an economy with a high EE rate may have higher productivity and thus a higher supply of goods and services.

As a result, the EE transition rate affects both aggregate demand and supply in the economy, and thus it’s potentially relevant for understanding inflation dynamics.

FRED has data that track the probabilities of EE transitions between two consecutive months. The FRED graph above shows two versions of these probabilities: the monthly probability in blue, which is highly volatile and seasonal, and its 3-month moving average in red, which is smoother by construction and allows for a better view of the trends. The Philadelphia Fed provides the data shown in the FRED graph above, backed by research from Fujita, Moscarini, and Postel-Vinay.

Several other research papers have investigated the role of EE transitions in inflation dynamics, including seminal work from Moscarini and Postel-Vinay. Work by Serdar Birinci, Fatih Karahan, Yusuf Mercan, and Kurt See contribute to this literature by studying the following:

• how the wealth distribution impacts the quantitative effects of EE transitions on inflation dynamics 
• how the monetary authority should respond to fluctuations in the EE transition rate

In particular, Birinci, Karahan, Mercan, and See highlight that periods with similar unemployment rates but different EE rates (as during the recessions shown in the graph above) should lead to different policy responses as the EE rate largely affects inflation dynamics.

How this graph was created: In FRED, search for and select “Average Probability of U.S. Workers Making Employer to Employer Transitions, Percent, Seasonally Adjusted.” From the “Edit Graph” panel in the top right corner, use the “Add Line” tab to search for and select “3-Month Moving Average of Average Probability of U.S. Workers Making Employer to Employer Transitions.”

Suggested by Serdar Birinci.