JTSLDL: US Layoffs and Discharges Monthly JOLTS Level from FRED (2000–2026)

The US Layoffs & Discharges series counts involuntary separations each month, from the BLS Job Openings and Labor Turnover Survey (JOLTS) — monthly since December 2000. Its range tells the story: a normal month sees 1.7–2 million layoffs, March 2020 saw 13.0 million, and April 2022 set the record low of 1.31 million. At 1.7 million in mid-2026, layoffs remain below their 2019 average — the fact that separates the current labor cooling from every recessionary episode in the series.

Dataset: US Layoffs & Discharges — JOLTS (2000–2026) · Updated 2026-05-01

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

Macro Takeaway

Dataset Overview

Dataset Variables

The CSV and Excel files contain the following columns.

A value of 1,708 means 1.71 million involuntary separations during the month.

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 JTSLDL:

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

Direct CSV Access — Eco3min Structured Dataset

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

print(f"Latest: {df['layoffs_discharges'].iloc[-1]/1000:.2f} million/month")
print(df.tail())

Using the Dataset in R

library(readr)

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

tail(df)
summary(df$layoffs_discharges)

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

Methodology

In JOLTS, layoffs and discharges cover all involuntary separations initiated by the employer: layoffs with or without recall intent, terminations for cause, and discharges from position eliminations. Quits (voluntary) and “other separations” (retirements, deaths, disability, transfers) are counted separately.

The published figure is a flow over the reference month, seasonally adjusted, from a survey of roughly 21,000 establishments.

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

Data Quality & Provider Notes

• Release latency. ~5 weeks after the reference month, one month behind payrolls and several weeks behind claims data.
• Revisions policy. Prior month revised each release; annual benchmarking. Moves under ~150k are within noise.
• Response rate. Post-2020 response rates near 30% widen single-print uncertainty.
• The 2020 scale break. March–April 2020 (13.0M and 8.8M) dwarf the rest of the series and distort any linear chart or average that includes them — standard practice is to plot them clipped or on log scale.
• Known gaps. None; continuous since December 2000.

Common Pitfalls When Using Layoffs Data

1. Extrapolating from announcement trackers. Company layoff announcements (tech trackers, WARN notices) cover a small, unrepresentative slice. JOLTS counts ~1.7–2M involuntary separations monthly even in strong markets; announcements are not the aggregate.
2. Reading low layoffs as a safe labor market. Unemployment can rise with layoffs at record lows if hiring slows — the 2024–2026 pattern. Layoffs measure one exit channel, not overall health.
3. Including 2020 in averages. The pandemic prints are 7–8 standard deviations from the mean; any statistic computed across them without exclusion is dominated by two months.
4. Confusing discharges with layoffs proper. The series merges economic layoffs and for-cause terminations; it cannot isolate ‘restructuring’ from routine turnover.

Historical Regimes

2001–2003 — Dot-com attrition. Layoffs ran near 2.0–2.2 million through the tech bust — elevated but gradual, a slow bleed rather than a cliff.

2004–2007 — Mid-cycle calm. The flow settled toward 1.8–1.9 million, the pre-GFC baseline.

2008–2009 — The genuine bust. Layoffs climbed to 2.65 million (April 2009) while quits collapsed and hiring froze — all three flows deteriorating together, the full recessionary signature.

2010–2019 — The long decline. A decade of falling layoffs to a 1.8 million average in 2019, then the lowest levels of the pre-pandemic record.

2020 — The vertical month. 13.0 million layoffs in March 2020 — more involuntary separations in one month than in the previous six combined — followed by 8.8 million in April, mostly temporary layoffs later recalled.

2021–2026 — Labor hoarding. The record low of 1.31 million (April 2022) came at peak tightness; even through 2023–2026 cooling, layoffs held near or below the 2019 norm — firms adjusting via hiring freezes rather than separations.

Related Macroeconomic Datasets

Layoffs are the involuntary exit flow; claims data provide the weekly lead, and the other JOLTS series the context.

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 JTSLDL

Dataset Reference