JOLTS Report

The JOLTS report (Job Openings and Labor Turnover Survey) is a monthly Bureau of Labor Statistics survey tracking job openings, hires, quits, separations, and layoffs. It provides a granular view of labor market dynamics — revealing whether weakness is on the demand side (fewer job openings) or supply side (workers quitting).

What JOLTS measures

The JOLTS report tracks five key flows:

Job openings:

• The number of jobs that employers are actively trying to fill.
• High openings relative to unemployment = tight labor market.
• Low openings = slack.

Hires:

• The number of workers hired in the month.
• Should roughly equal separations in steady state.
• Higher hiring = economic strength.

Quits (voluntary separations):

• Workers voluntarily leaving jobs.
• High quit rate = workers confident in job market.
• Low quit rate = workers fearful (recession coming).

Layoffs and discharges:

• Involuntary separations by employer.
• High layoffs = weakness (recession).
• Low layoffs = strength.

Other separations:

• Workers retiring, going to school, relocating, etc.

The Beveridge curve

The most famous JOLTS application is the Beveridge curve, plotting job openings against unemployment:

• Tight markets: Many openings relative to unemployed; the economy is booming and workers are scarce.
• Slack markets: Few openings relative to unemployed; many workers are chasing few jobs.

Normally, the relationship is inverse — as unemployment falls, openings rise. But the curve can shift. In 2022-23, the curve shifted outward (more openings for the same unemployment), suggesting structural mismatch — jobs available but workers lack skills.

The Beveridge curve shift raised debate: Is unemployment at the natural rate when you have 8+ million openings and 6 million unemployed? The Fed’s answer: probably yes, implying high structural unemployment.

JOLTS in the business cycle

Expansions:

• Job openings rise.
• Quits rise (workers confident they can find better jobs).
• Layoffs fall.
• JOLTS paints a picture of a tightening labor market.

Recessions:

• Job openings fall sharply.
• Quits fall (workers fearful).
• Layoffs spike.
• JOLTS clearly shows the deterioration.

COVID-19 example:

• Feb 2020: 7.5 million job openings, 5.5 million unemployed.
• April 2020: Openings fell to 4.6 million, unemployed spiked to 23 million (implied).
• By mid-2022: Openings hit 11.7 million (a record), unemployed fell to 3.7 million — extreme tightness.

Why JOLTS matters for policy

The Federal Reserve watches JOLTS to assess:

1. Demand-side weakness: Are openings falling? = weak demand, time to cut rates.
2. Supply-side tightness: Are openings high relative to unemployed? = tight labor markets, time to raise rates.
3. Wage pressure: High quit rates + few openings = workers have bargaining power, likely wage acceleration.
4. Inflation risk: Tight labor markets (many openings, few unemployed) = wage pressure = inflation risk.

In 2021-22, JOLTS showed extreme tightness: millions of more openings than unemployed. This signaled to the Fed that inflation was a major risk and rates needed to rise quickly.

Quits as economic indicator

The quit rate is particularly informative:

High quit rate signals:

• Worker confidence in job market.
• Workers seeking better opportunities.
• Likely wage acceleration (workers have power).
• Possible inflation ahead.

Low quit rate signals:

• Worker fear/pessimism.
• Reluctance to search for new jobs.
• Likely wage deceleration.
• Possible deflation ahead.

During the “Great Resignation” (2021-22), quit rates hit record highs as workers felt empowered by tight labor markets. This was later seen as a warning signal that the Fed should have tightened sooner.

Limitations of JOLTS

JOLTS, like all surveys, has limitations:

• Lag: Data are released ~30 days after the month ends, so real-time policy is a bit behind.
• Revisions: Large revisions are common, sometimes changing the narrative significantly.
• Definition ambiguity: What counts as an “opening”? Firms might post a job but not actively recruit. Measurement varies.
• Doesn’t capture quality: An opening for a $30k job and a $100k job count the same.
• Doesn’t capture spatial mismatch: Plenty of openings in Austin; plenty of unemployed in Baltimore. But they don’t meet.

JOLTS and the matching function

Economists use JOLTS data to estimate the labor market “matching function” — how many hires result from a given number of openings and unemployed:

Hires ≈ f(Job openings, Unemployed)

If the matching function has deteriorated (fewer hires from same openings + unemployed), it suggests structural mismatch or reduced frictional efficiency. Evidence for this deterioration post-2021 suggests that mismatches between jobs and workers’ skills/location are substantial.

See also