JTSJOL: US Job Openings Monthly JOLTS Level from FRED (2000–2026)

The US Job Openings series counts total nonfarm job openings on the last business day of each month, from the BLS Job Openings and Labor Turnover Survey (JOLTS) — monthly since December 2000. It is the demand side of the US labor market in one number: 12.3 million openings at the March 2022 peak, 2.2 million at the July 2009 trough, 7.6 million in mid-2026. Alongside the quits rate and the vacancy-to-unemployed ratio, it forms the JOLTS triad that reshaped how tightness is measured after the pandemic.

Dataset: US Job Openings — JOLTS (2000–2026) · Updated 2026-05-01

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

Macro Takeaway

Dataset Overview

Dataset Variables

The CSV and Excel files contain the following columns.

A value of 7,594 means 7.59 million open positions.

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 JTSJOL:

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

Direct CSV Access — Eco3min Structured Dataset

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

print(f"Latest: {df['job_openings'].iloc[-1]/1000:.2f} million openings")
print(f"Peak: {df['job_openings'].max()/1000:.2f}M on {df.loc[df['job_openings'].idxmax(), 'date'].date()}")

Using the Dataset in R

library(readr)

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

tail(df)
summary(df$job_openings)

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

Methodology

JOLTS surveys roughly 21,000 nonfarm establishments monthly. A job opening is counted when a specific position exists, work could start within 30 days, and the employer is actively recruiting externally. The published figure is the seasonally adjusted count on the last business day of the reference month — a stock, unlike hires and quits, which are flows over the month.

The series begins in December 2000; there is no comparable official vacancy count before that (researchers splice the Conference Board Help-Wanted Index for earlier history, a different instrument).

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. JOLTS publishes with roughly a five-week lag — about one month behind the payrolls report for the same reference month.
• Revisions policy. The prior month is revised with each release; annual benchmarking and seasonal-factor updates adjust recent years. Month-to-month moves of a few hundred thousand are within normal revision noise.
• Response rate. JOLTS response rates declined materially after 2020 (from ~60% to ~30%), widening confidence intervals. Level readings are more uncertain than pre-pandemic; multi-month trends remain informative.
• Alternative sources. Indeed Job Postings Index (daily, online postings only) leads JOLTS by weeks but measures a different universe; LinkUp and Lightcast offer scraped-posting alternatives.
• Known gaps. None since inception; the series starts December 2000.

Common Pitfalls When Using Job Openings

1. Treating an opening as a guaranteed hire. Openings measure recruiting intent, not completed matches. The openings-to-hires gap widened notably after 2021, feeding the “ghost postings” debate — postings kept live without active intent. The survey definition requires active recruiting, but incentives to over-post are real.
2. Confusing the stock with the flows. Openings are a point-in-time stock; hires and quits are monthly flows. Comparing them directly without noting the distinction produces incoherent “openings vs hires” arithmetic.
3. Reading levels without the unemployment denominator. 7.6 million openings means something different with 6 million unemployed than with 10 million. The vacancy-to-unemployed ratio is the tightness-adjusted read.
4. Over-reading single prints. Low response rates and routine revisions make one-month moves unreliable; three-month averages are the standard smoothing in Fed communication.

Historical Regimes

2001–2003 — Dot-com aftermath. The series opened into a downturn: openings fell from ~5 million toward 3 million as the tech bust worked through hiring, well before the unemployment rate peaked in mid-2003.

2004–2007 — Mid-cycle expansion. Openings recovered to the 4–4.7 million range — the pre-GFC norm for a tight-but-not-overheated labor market.

2008–2009 — The collapse. Openings halved in eighteen months to the series trough of 2.23 million (July 2009) — barely one opening for every seven unemployed workers at the depth of the crisis.

2010–2019 — The long climb. A near-monotonic decade of recovery took openings past 7 million by 2018 — the year openings first exceeded the number of unemployed in the series’ history. The 2019 average near 7.2 million defined the pre-pandemic benchmark.

2020 — Crash and V-rebound. Openings dropped to ~4.6 million in April 2020, then rebounded within months as reopening demand met constrained labor supply.

2021–2022 — The vacancy boom. Openings blew through every prior record to peak at 12.30 million in March 2022 — the tightest labor market in the postwar record by this measure, and the statistical heart of the Great Resignation alongside the quits rate peak.

2023–2026 — Normalization without a bust. Openings drifted down to 7.6 million by mid-2026 — a decline of nearly 40% from peak achieved mostly through fewer postings rather than layoffs, returning the series to its late-2010s range.

Related Macroeconomic Datasets

Openings are the demand-side stock; the flows (quits, claims) and the supply side (unemployment) complete the picture.

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 JTSJOL

Dataset Reference