JTSQUR: US Quits Rate Monthly JOLTS Data from FRED (2000–2026)

The US Quits Rate measures voluntary job separations as a percent of total nonfarm employment, monthly since December 2000, from the BLS Job Openings and Labor Turnover Survey (JOLTS). It is the labor market’s confidence gauge: workers quit when they believe a better job is available. The series peaked at 3.0% in late 2021 — the statistical signature of the Great Resignation — bottomed at 1.2% in August 2009, and stood at 1.9% in mid-2026.

Dataset: US Quits Rate — JOLTS (2000–2026) · Updated 2026-05-01

Source: US Bureau of Labor Statistics, JOLTS · FRED series JTSQUR

Macro Takeaway

Dataset Overview

Dataset Variables

The CSV and Excel files contain the following columns.

A value of 1.9 means 1.9% of employed workers voluntarily left their job during the month — roughly 3 million people.

Download the Complete Dataset

The full dataset is available in CSV and Excel formats.

FRED Direct CSV Access

The underlying data is available from FRED under series code JTSQUR:

https://fred.stlouisfed.org/graph/fredgraph.csv?id=JTSQUR

Direct CSV Access — Eco3min Structured Dataset

https://eco3min.fr/dataset/us-quits-rate.csv

This URL returns the complete dataset in CSV format. It can be used directly in pandas, R, curl, or any data tool.

Using the Dataset in Python

import pandas as pd

url = "https://eco3min.fr/dataset/us-quits-rate.csv"
df = pd.read_csv(url, parse_dates=["date"])

print(f"Latest: {df['quits_rate'].iloc[-1]:.1f}%")
print(f"Peak: {df['quits_rate'].max():.1f}% on {df.loc[df['quits_rate'].idxmax(), 'date'].date()}")

Using the Dataset in R

library(readr)

url <- "https://eco3min.fr/dataset/us-quits-rate.csv"
df <- read_csv(url)

tail(df)
summary(df$quits_rate)

Both examples load the dataset directly from the URL — no download or API key required.

Methodology

In JOLTS, a quit is a voluntary separation initiated by the employee — resignations, but not retirements (counted in “other separations”) and not layoffs or discharges (counted separately). The quits rate divides the month’s quits by total nonfarm employment. It is a flow over the month, unlike job openings, which are a stock at month-end.

The rate rather than the level is the standard measure because it normalizes for employment growth: 3 million quits means different things in a 130-million-job economy and a 160-million-job economy.

This dataset is updated daily (Mon–Sat, 08:00 UTC) via automated pull from the FRED API; new observations appear with each monthly JOLTS release.

Data Quality & Provider Notes

• Release latency. Roughly five weeks after the reference month, one month behind the payrolls report.
• Revisions policy. Prior month revised each release; annual benchmark and seasonal updates. The rate, reported to one decimal, is less revision-noisy than the levels.
• Response rate. JOLTS response rates fell from ~60% pre-pandemic to ~30%, widening uncertainty around single prints; multi-month trends remain robust.
• Alternative sources. ADP and LinkedIn publish private turnover measures; the Atlanta Fed Wage Growth Tracker’s job-switcher series is the standard companion for the quits-to-wages channel.
• Known gaps. None since inception (December 2000).

Common Pitfalls When Using the Quits Rate

1. Reading quits as distress. Rising quits signal confidence, not trouble — the opposite of layoffs. Headlines conflating “record separations” with labor-market weakness in 2021 inverted the signal.
2. Ignoring sector composition. The aggregate rate is dominated by high-turnover sectors (leisure and hospitality routinely runs above 4%; government below 1%). Aggregate moves can reflect sector mix as much as broad behavior.
3. Confusing quits with total separations. Total separations add layoffs, discharges, retirements, and other exits. Using the separations series where quits are meant mixes voluntary and involuntary signals.
4. Expecting a mechanical wage link month-to-month. The quits-to-wage-growth relationship operates over quarters, through the job-switcher premium — not print by print.

Historical Regimes

2001–2003 — Post-bubble caution. The series opened around 2.5% and slid toward 1.8% as the dot-com bust made workers hold their positions.

2004–2007 — Mid-cycle churn. Quits stabilized in the 2.0–2.2% range — the pre-GFC norm for healthy turnover.

2008–2009 — Confidence collapse. The rate fell to the series low of 1.2% (August 2009): with hiring frozen, almost nobody left a job voluntarily. Quits stayed below 1.5% into 2011 — the slowest labor-market churn on record.

2010–2019 — The decade-long rebuild. A slow, steady recovery took the rate back to 2.3% by 2019 — full-employment churn restored after nearly a decade.

2020 — The pandemic dip. Quits briefly collapsed to 1.5% (April 2020) as uncertainty froze voluntary movement, then rebounded within months.

2021–2022 — The Great Resignation. The rate hit 3.0% in November 2021 and again in April 2022 — roughly 4.5 million quits per month at the peak. Record openings, accumulated savings, and remote-work reshuffling produced the highest voluntary-turnover episode in the series, with the job-switcher wage premium at multi-decade highs.

2023–2026 — The Big Stay. Quits normalized below the pre-pandemic baseline, reaching 1.9% by mid-2026 — a low-churn equilibrium in which both hiring and firing run cold, and workers extract fewer gains from switching.

Related Macroeconomic Datasets

Quits read best against the demand stock (openings), the involuntary flow (claims), and the wage outcome they lead.

Macroeconomic Dataset Hub

This dataset is part of the Eco3min macro-financial data repository.

Sources

• US Bureau of Labor Statistics — Job Openings and Labor Turnover Survey (JOLTS)
• Federal Reserve Bank of St. Louis — FRED series JTSQUR

Dataset Reference