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What Does the JOLTS Report Tell Us?

Every month, the U.S. Bureau of Labor Statistics releases the Job Openings and Labor Turnover Survey—JOLTS for short. While the unemployment rate captures how many people are out of work, the JOLTS report captures the other side of the story: how many jobs are available, how often workers quit, and how freely labor flows between employers. The report has become a central tool for understanding labor market health. A high number of job openings relative to unemployed workers signals a tight market where employers struggle to fill vacancies. High quit rates reveal worker confidence—when people are quitting jobs at high rates, they're usually doing so because they expect to find something better. The JOLTS report also captures gross flows (how many people hire, fire, and transition), not just net changes in employment. This granular view has made it invaluable to the Federal Reserve, economists, and policymakers trying to understand inflation risks and labor market slack.

Quick definition: The JOLTS report measures job openings, hiring, separations (quits and layoffs), and transfers—providing a real-time snapshot of labor market dynamism and worker confidence beyond the headline unemployment rate.

Key takeaways​

  • The JOLTS report tracks four flows: hires, separations (quits and layoffs), job openings, and transfers between establishments.
  • A high ratio of job openings to unemployed workers (above 1:1) signals tight labor markets where employers compete for workers.
  • Quit rates indicate worker confidence; high quits suggest workers expect better opportunities and are willing to change jobs.
  • JOLTS data is published monthly by the BLS, with a one-month lag, and covers roughly 21,000 establishments representing ~1 million workers.
  • The report has become critical for Fed rate-setting decisions, especially when the unemployment rate is low but traditional labor market measures seem misaligned.
  • Job openings can overheat an economy if they stay high relative to unemployment, signaling demand outpacing supply for labor.

The Origins and Purpose of JOLTS​

The JOLTS program began in December 2000, created by the Bureau of Labor Statistics to fill a gap in labor market data. The unemployment rate tells you the stock of unemployed people, but it says nothing about the flow: how many people are hired, how many quit, how many jobs are created or destroyed. Economists and policymakers realized they were flying partly blind. They could see the end result (a 3% unemployment rate), but not the underlying dynamics that got there.

JOLTS aimed to paint a fuller picture. Each month, the BLS surveys roughly 21,000 private and public establishments (though the sample represents about 1 million actual workers through weighting). Surveyed firms report:

  • Hires: how many workers they added (counts start and rehires)
  • Separations: how many workers left, split into quits (resignations) and layoffs/discharges
  • Job openings: the number of jobs vacant on the last business day of the month, in need of staffing
  • Transfers: workers who changed jobs within the same company

The BLS then aggregates these microdata into national figures, all published with a one-month lag at FRED (Federal Reserve Economic Data). The result is a monthly report that shows labor market dynamism in real time.

The Core Metrics: What Each Number Means​

Job openings are perhaps the most widely watched JOLTS metric. The number represents unfilled positions—jobs that firms are actively trying to fill on the reference date (the last day of the month). In a slack labor market, job openings are low relative to unemployment. In a tight market, openings exceed unemployment. The openings-to-unemployed ratio—often called the Beveridge curve ratio—is a key Fed indicator. When this ratio exceeds 1:1 (more openings than unemployed workers), the labor market is extremely tight.

To illustrate: in January 2022, there were 10.9 million job openings and 6.3 million unemployed workers, a ratio of 1.73:1. Even accounting for frictional unemployment and skills mismatch, this gap suggested that every unemployed worker could theoretically find a job, with employers still searching for more. By contrast, in July 2009 (post-financial-crisis), there were 2.4 million openings and 15 million unemployed, a ratio of 0.16:1—a labor market with enormous slack.

Hires capture how many workers firms bring onto payroll each month. This flows into the net job creation figure (hires minus separations), which determines whether the economy is adding jobs overall. Hires tend to be cyclical: they spike early in expansions and slow as economies mature. A sustained high hire rate, even as unemployment is low, can signal ongoing labor scarcity.

Quits are resignation-driven separations. Workers quit when they have options—when they believe they can find a better job elsewhere, get a raise by switching employers, or improve their working conditions. Quit rates are therefore a confidence indicator. In the 2008–2009 recession, the quit rate fell to 1.2% (meaning roughly 1.2% of employed workers quit per month), as workers feared job-hunting in a depression. In early 2022, the quit rate rose above 3%, nearly double the recession rate, reflecting worker confidence and job abundance.

A "The Great Resignation" label was applied to 2021–2022 when quit rates spiked. While the term was catchy, the data tell a more nuanced story. The spike partly reflected normal labor market rebalancing after COVID—workers who had been furloughed or teleworking were testing new opportunities. It also reflected genuine wage growth in some sectors (hospitality, retail, healthcare), making switching attractive. But it also revealed frustration with remote work policies, health concerns, and burnout in essential-worker jobs. The quit spike was real, but temporary; quit rates have moderated since 2023.

Layoffs and discharges capture involuntary separations. These are cyclical; they spike when recessions hit and firms shed workers, then fall during expansions. The hiring and separation numbers tell a complete flow story: hiring remains important even in tight labor markets (firms continue to recruit), and layoffs persist even during expansions (some firms shrink while others grow).

The Beveridge Curve and Labor Market Slack​

The Beveridge curve, named after economist William Beveridge, plots the relationship between unemployment and job openings. In normal times, the curve slopes downward: when unemployment is high, openings are low, and vice versa. The curve represents the trade-off inherent in any labor market—as employers fill vacancies by hiring unemployed workers, unemployment falls and openings fall (as vacancies get filled).

The curve itself can shift. In the 2010s, the U.S. Beveridge curve shifted inward, meaning fewer openings for any given unemployment rate. This suggested that labor market slack was larger than the headline unemployment rate indicated—there was mismatch, skills gaps, or geographic misalignment that kept unemployed workers from filling available jobs. The shift was consistent with the Phillips curve flattening: a looser link between unemployment and wage pressure, because many jobless workers couldn't easily transition into open roles.

By 2021–2022, the Beveridge curve had shifted back outward, with far more openings relative to unemployment than before. Economists interpreted this as a genuinely tight labor market. The Fed cited the high openings-to-unemployed ratio as evidence that inflation was demand-driven and that tightening rates was necessary.

The Beveridge curve's movements reveal something crucial: the economy's actual slack is not simply the unemployment rate. A 5% unemployment rate with 6 million job openings is far tighter than a 5% rate with 4 million openings. JOLTS data makes this visible.

Quit Rates as an Economic Signal​

The quit rate has emerged as one of the most revealing indicators of labor market health and worker sentiment. When workers are quitting at high rates, especially voluntarily to take other jobs (rather than to retire or leave the workforce), it signals several things:

Confidence in future prospects. A worker who quits is betting that something better awaits. This is risky behavior in a weak economy; unemployed job-hunters spend months searching. A high quit rate means most workers feel secure that they'll land quickly.

Wage pressure. When workers switch jobs, they often do so for higher pay. High quit rates correlate with wage growth; employers must raise pay to retain staff if switching is rewarding elsewhere. From the Fed's perspective, a rising quit rate is an early-warning signal for inflation.

Sectoral shifts. Quit rate spikes are rarely uniform across industries. In 2021–2022, the spike was concentrated in leisure and hospitality (restaurants, bars, hotels), retail, and healthcare. These sectors faced staffing challenges, partly due to wage offers improving. Manufacturing quit rates remained more moderate. This tells policymakers where labor is reallocating and which sectors are tight.

Worker bargaining power. High quit rates suggest workers have leverage. Firms must improve offers to retain staff. This translates into better negotiating positions for workers—higher pay, better benefits, more flexible scheduling. Economists debate whether this is "good" (workers gaining), but from an inflation perspective, rising quit rates and rising wages often move together.

A key nuance: quit rates fell during the pandemic (2020), not because workers were happy, but because unemployment spiked and risk aversion rose. The sharp rebound in quits as the job market tightened likely reflected a mix of factors—catch-up hiring, reluctance to return to in-person work, and real wage improvements in some sectors. Unlike the pandemic unemployment collapse, which was involuntary, the quit spike was more about workers exercising choice.

Hires, Separations, and Gross Labor Flows​

While net job creation (hires minus separations) makes headlines, the gross flows tell the true complexity of the labor market. The economy constantly churns. In a typical month with 300 million workers, roughly 5–6 million people are hired and 4–5 million separate (quit or are laid off). Net job creation of <500,000 is the difference between these huge gross flows.

This churn matters because:

Frictional unemployment exists. Even in a perfectly functioning labor market, there is always some unemployment because job-seeking takes time. A worker who quits one job and takes a month to find another is frictionally unemployed. The JOLTS report, combined with unemployment data, lets economists estimate how much unemployment is frictional versus cyclical (due to weak demand).

Job destruction and creation are simultaneous. A weak sector might be cutting jobs while a strong sector is hiring. The net figure masks this; JOLTS helps reveal it. In 2020, many service jobs were destroyed while goods-producing jobs grew (shifting consumption toward durable goods). Without JOLTS, the net figure would be less informative.

Demographic flows matter. Hires include not just workers who are rehired after separation but also new entrants to the labor force. Separations include not just people switching jobs but some retirements and exits. JOLTS data, combined with other BLS surveys, helps decompose these.

Hire rates vary over the business cycle in predictable ways. Early in an expansion, hire rates are high (firms are growing). Late in an expansion, as unemployment falls and growth slows, hire rates are lower (firms are more cautious). The pattern helps economists gauge where the cycle stands.

How the Fed Uses JOLTS​

The Federal Reserve cited JOLTS data heavily from 2021 to 2023 when deciding on interest rate policy. When officials argued the labor market was "very tight" and inflation was "demand-driven," they pointed to JOLTS data:

  • Job openings were near record highs (10+ million in 2021–2022).
  • Quit rates were elevated, suggesting wage pressure.
  • The Beveridge curve was shifted far outward.

By these measures, the economy was overheating. The labor market couldn't absorb the excess demand without inflation rising. The Fed's response was to raise the federal funds rate from near-zero in March 2022 to over 5% by mid-2023. JOLTS data was central to the case.

As the labor market loosened in late 2023–2024 (job openings fell, quit rates normalized), the Fed cited the same JOLTS metrics as evidence that tightening was working. This illustrates how important JOLTS has become. The unemployment rate alone didn't capture the intensity of labor market tightness; only JOLTS data did.

Limitations of JOLTS​

Like any survey, JOLTS has limitations:

One-month lag. The report is published with a one-month delay, which can make it less useful for real-time policy than policymakers prefer. By the time the January openings data is released in February, the economy has already moved forward.

Margin of error. Like all BLS surveys, JOLTS estimates have confidence intervals. Individual month-to-month changes can be noisy. The BLS publishes 3-month moving averages to smooth volatility.

Sample size. While 21,000 establishments is substantial, it represents only a fraction of millions of U.S. businesses. Very small firms (under 5 employees) are underrepresented.

Definition challenges. What counts as a "job opening"? Firms self-report. Some may count hoped-for positions they expect to fill soon; others may count only immediately-available slots. The definition can shift with business confidence, creating optical shifts that don't reflect real changes.

Cyclical bias. Job openings tend to be reported more during booms (when firms are optimistic they can fill them) and less during recessions (when uncertainty depresses reporting). This can make the Beveridge curve appear to shift even if true underlying conditions haven't changed.

Doesn't capture quality. JOLTS counts jobs but doesn't measure job quality, wage offers, or benefits. A reopened position at a lower wage than its predecessor still counts as one opening.

Real-world examples​

The 2021–2022 period offers a concrete illustration of JOLTS's power. In January 2022, job openings stood at 10.9 million while unemployment was 6.3 million (accounting for total unemployed). The headlines said unemployment was only 4%—low by historical standards. But JOLTS revealed a much tighter picture: there were nearly 2 openings per 1 jobless person. Employers were scrambling; multiple job offers per worker were common in many fields.

This tightness was temporary. By late 2023, job openings had fallen to 8.7 million while unemployment had risen to 4%, a ratio closer to historical norms. The tightness eased; the labor market rebalanced. Without JOLTS data, the loosening would have been harder to discern.

The 2008–2009 recession also illustrates JOLTS value. Openings collapsed from 3+ million to 1.9 million, while unemployment soared. The Beveridge curve shifted far inward—a sign of severe economic slack. This graph, plotted with JOLTS data, was a powerful visual for explaining why the recession was so severe.

The pandemic shock of 2020 created a unique JOLTS situation. Layoffs spiked (from 1.5% to 2.7%), hires collapsed, and quit rates fell as workers hunkered down. But as economies reopened, hires rebounded sharply while layoffs returned to historical norms. Quits then spiked, creating the unusual situation where both separations (quits) and hires were simultaneously elevated in 2021–2022. This pattern—high churn in both directions—was distinctive and revealed structural shifts in labor allocation, not just cyclical employment growth.

Common mistakes​

Mistake 1: Confusing job openings with job creation. A new job opening is not the same as a net new job. If a firm posts 10 openings to fill 10 departed positions, that's zero net job creation but 10 openings reported. JOLTS openings are a stock (the number of unfilled positions on one day), while job creation is a flow (net hiring over a month).

Mistake 2: Assuming all job openings are filled. Some openings persist unfilled for months, especially for high-skill roles, jobs in remote areas, or positions with very low wage offers. A 10 million opening count doesn't mean 10 million jobs will be filled soon.

Mistake 3: Taking quit rates as purely positive. While high quits can reflect worker confidence, they also reflect friction and potential losses from disrupted productivity. If a firm's best engineer quits, that's a loss even if the engineer finds a better role elsewhere.

Mistake 4: Ignoring sectoral heterogeneity. Aggregate quit rates hide the fact that quits are concentrated in certain industries. In 2021–2022, leisure and hospitality saw quit spikes while healthcare saw high but more moderate increases. Aggregate numbers can mask important sector-specific trends.

Mistake 5: Assuming JOLTS captures all job availability. Some job openings are never formally posted (internal promotions fill them) or are posted informally (word of mouth in tight professions). The formal JOLTS count may understate true available opportunities.

FAQ​

How often is the JOLTS report released?​

The JOLTS report is released monthly by the Bureau of Labor Statistics, usually on the first Tuesday of the month (for data from two months prior). Each release includes data for 10 series going back roughly three years.

How does JOLTS data affect the Federal Reserve's decisions?​

The Fed looks at JOLTS—particularly job openings and quit rates—to assess labor market tightness. If openings are elevated relative to unemployment and quit rates are high, the Fed interprets this as inflation risk and may raise rates. The Fed publishes summaries of economic conditions (the Beige Book) that often reference JOLTS.

What is a typical job opening-to-unemployed ratio?​

In normal times, the ratio is roughly 0.5–0.8:1 (fewer openings than unemployed). A ratio above 1:1 is considered very tight. In late 2021–early 2022, it hit 1.7–1.8:1, an extreme level. The Fed viewed this as unsustainable and a reason for tightening.

Why do quit rates matter more than layoff rates for inflation?​

Quits indicate worker-driven transitions based on opportunity, while layoffs are firm-driven and often reflect weak demand. High quits suggest workers are being sought by other employers (wage competition), whereas high layoffs might suggest weakness. For inflation purposes, quits are more concerning because they signal tight supply of labor and wage pressure.

Has JOLTS helped or hurt Fed policy?​

That's debated. Proponents argue JOLTS data revealed the true extent of labor market tightness in 2021–2022, helping the Fed justify rate hikes before inflation became embedded. Critics argue the Fed over-relied on JOLTS to declare the labor market "hot" and potentially over-tightened. The honest answer: JOLTS provided essential data, but interpretation was contestable.

Is JOLTS better than the unemployment rate for measuring labor market slack?​

Neither is "better" in isolation. The unemployment rate captures those actively job-seeking; JOLTS captures available positions and worker transitions. Together, they provide a fuller picture. The Fed increasingly uses both.

Summary​

The JOLTS report, published monthly by the BLS, tracks job openings, hires, separations (quits and layoffs), and transfers. These data reveal labor market dynamism beyond the headline unemployment rate. High job openings relative to unemployment signal tight markets; high quit rates reveal worker confidence and wage pressure. The Federal Reserve has increasingly relied on JOLTS to assess whether the labor market is overheating and whether inflation is demand-driven. While JOLTS has limitations—monthly lags, survey sample errors, and reporting bias—it has become indispensable for modern labor market analysis and monetary policy.

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