Guide · WARN Act
How to Analyze Mass Layoff Trends
A framework for interpreting WARN Act data, what industry patterns, seasonal cycles, and geographic concentrations reveal about economic shifts.
2026 has recorded 809 WARN Act notices so far
According to WARN Act filings submitted to state workforce agencies, 2026 has recorded 809 notices affecting 73,974 workers year-to-date, versus 1,677 notices (145,934 workers) for the full 2025 year. The current year is not yet complete, so a lower running total does not by itself mean layoffs have slowed; year-over-year totals also shift with economic cycles and how promptly each state publishes its data.
Single-month spikes mislead, compare a filing against the same month in prior years and against peers in the same industry before calling a structural shift.
Amazon filing #1 is not United Airlines workers #1
According to WARN Act filings submitted to state workforce agencies, Amazon ranks #1 of 7,009 by recorded WARN filing count (114 notices, 41,969 workers, workers rank #2). United Airlines ranks #1 of 7,009 by workers on notice (42,706 workers across 9 filings, filing rank #35). The most frequent filer is not the largest worker total.
- Amazon 114 filings · workers rank #2
- United Airlines 42,706 workers · filing rank #35
The short answer
A single quarter can swing the totals because one large employer filing moves the line, which is why trend-reading the WARN data means watching concentration and timing, not just the monthly count.
By the numbers
The shape of the WARN record
- 9,006
- WARN notices tracked
- 1,433,550
- Workers on notice
- 7,035
- Employers filing
Most filings is not most workers
Recorded WARN notice count for the filing #1 and workers #1 employers among 7,009 in this pool
- Amazon
Amazon
114 filings
- United Airlines 9
United Airlines
9 filings
What this shows Amazon leads the filing-count ranking; United Airlines leads workers on notice in the same pool.
States with the most workers on WARN notice
Total workers listed on WARN notices, by state of filing, federal & state WARN-Act records
- Texas
Texas - 286,423 workers across 2,414 notices
286,423 workers
- Washington
Washington - 237,737 workers across 1,071 notices
237,737 workers
- California
California - 201,932 workers across 1,205 notices
201,932 workers
- Indiana
Indiana - 151,425 workers across 1,001 notices
151,425 workers
- Virginia
Virginia - 124,054 workers across 1,024 notices
124,054 workers
- Oregon 65,152
Oregon - 65,152 workers across 379 notices
65,152 workers
- Maryland 61,606
Maryland - 61,606 workers across 629 notices
61,606 workers
- Illinois 58,300
Illinois - 58,300 workers across 5 notices
58,300 workers
Why This Matters
Warn act mass layoff tracking data is increasingly important for workers, job seekers, journalists, policymakers. However, raw data without context can be misleading. Numbers that appear alarming may reflect normal patterns when viewed in historical context, and seemingly stable figures may hide significant underlying shifts. This guide provides the framework for interpreting the data on PlainLayoffs with appropriate nuance.
The challenge is that WARN Act mass layoff tracking data comes from government sources (U.S. Department of Labor / State Workforce Agencies) that were designed for regulatory compliance and statistical reporting, not for the questions that most people are actually trying to answer. Understanding the gap between what the data measures and what you need to know is essential for drawing valid conclusions.
Key Concepts
What the data captures: Official records from U.S. Department of Labor / State Workforce Agencies provide a structured view of WARN Act mass layoff tracking across the United States. These records follow standardized reporting requirements, which means the data is consistent and comparable across geographic areas and time periods. This consistency is the primary strength of government data, it enables apples-to-apples comparison.
What the data misses: No dataset captures everything. Government reporting has coverage gaps, reporting delays, and definitional boundaries that exclude certain activities or populations. Always check the scope and coverage notes on our about page before drawing conclusions from the data.
How to contextualize: Numbers are most meaningful when compared, against historical baselines, geographic peers, or industry averages. A figure that looks high in isolation may be perfectly normal for its category. Always compare within the appropriate reference group.
Practical Steps
Step 1, Start with the big picture. Before drilling into specific records, check the broad trends. What is the overall direction? Is the pattern you are investigating part of a larger trend or an isolated anomaly?
Step 2, Compare appropriately. When evaluating any specific data point on PlainLayoffs, compare it against similar entities rather than the national average. Geographic, industry, and size differences create natural variation that makes broad comparisons misleading.
Step 3, Check the source. Every data point on PlainLayoffs ultimately traces back to U.S. Department of Labor / State Workforce Agencies. When the stakes are high, career decisions, policy analysis, research publications, verify critical figures against the primary source. We provide source links on our data pages.
Step 4, Apply judgment. Data is a starting point, not an answer. The best decisions combine quantitative data with qualitative context, local knowledge, expert consultation, and direct observation. Use PlainLayoffs data to narrow your focus and inform your questions, not to replace professional judgment.
Common Misconceptions
One of the most frequent errors when working with WARN Act mass layoff tracking data is treating aggregate statistics as individual predictions. National or state-level averages describe populations, not specific cases. Your individual experience may differ significantly from what aggregate data suggests, and that is expected, averages compress enormous variation into a single number.
Another common mistake is assuming more recent data is always more relevant. Government data typically has a reporting lag. Depending on the dataset, the most recent available figures may describe conditions from 12-24 months ago. Current conditions may have shifted, particularly in rapidly changing sectors or regions.
Reading Layoff Trend Data Correctly
Year-over-Year vs. Month-over-Month
Year-over-year comparisons smooth out seasonal variation but can miss inflection points. Month-over-month data captures rapid shifts, like the January 2026 count of 226 notices, but is noisy. The most reliable approach combines both: use year-over-year for directional trends and month-over-month for timing signals.
Adjusting for Reporting Lag
Using Baselines for Comparison
A meaningful baseline requires at least 3 years of data. The 2017-2019 pre-pandemic period serves as a normal baseline for most industries. Comparing 2026 figures to 2020 (COVID peak) or 2021 (recovery) produces distorted conclusions. For detailed numbers, see our research hub.
Spotting Structural vs. Cyclical Shifts
Cyclical layoffs follow the business cycle and eventually reverse. Structural layoffs reflect permanent changes, automation, offshoring, or industry disruption. The retail sector illustrates this well: e-commerce growth has driven a structural decline in brick-and-mortar employment that no economic upswing will reverse. Our industry patterns guide breaks this down by sector.
WARN notices are not always filed promptly. Some states have 60-day filing requirements, meaning the most recent 2 months of data are always incomplete. California and Texas tend to file more quickly than Washington or Oregon. When analyzing trends, exclude the most recent 60 days to avoid undercount bias.
| Year | WARN Notices | Workers Affected |
|---|---|---|
| 2026 (YTD) | 809 | 73,974 |
| 2025 | 1,677 | 145,934 |
| 2020 | 2,647 | 455,174 |
Source: WARN notice data via PlainLayoffs, computed live from the live database. The current year is year-to-date, not a full-year total.
Frequently Asked Questions
What data does PlainLayoffs use?
PlainLayoffs uses data from U.S. Department of Labor / State Workforce Agencies. All data comes from public government sources and is processed through our ETL pipeline for searchability and analysis.
How often is the data updated?
We update our database as new data becomes available from U.S. Department of Labor / State Workforce Agencies. Update frequency depends on the source agency's release schedule, which varies from weekly to annually depending on the dataset.
Is PlainLayoffs free to use?
Yes. PlainLayoffs is completely free, requires no account, and is supported by non-intrusive advertising. We believe public data should be freely accessible.
Worked example: putting the notice period in context
Consider an employer planning a covered layoff with a separation date 60 days away. Federal WARN generally applies to employers with 100 or more employees and requires at least 60 calendar days' advance written notice for covered plant closings and mass layoffs. California's WARN rules generally require 60 days' notice for covered employers with 75 or more employees, while New York's WARN Act requires 90 days' notice for covered private businesses with 50 or more full-time employees. The applicable rule depends on the facts, including the employer, location, affected workforce, and any statutory exception.
Verified starting points
| Jurisdiction | General rule | Official guidance |
|---|---|---|
| Federal WARN | Generally 60 calendar days for covered plant closings and mass layoffs. | U.S. Department of Labor |
| California WARN | Generally 60 days for covered employers and covered events. | California EDD |
| New York WARN | Generally 90 days for covered private businesses. | New York Department of Labor |
A WARN notice is a legal notice, not a courtesy; the applicable federal and state rules should be checked before anyone relies on a date.
How to use PlainLayoffs data to understand your situation
Start with the WARN Act overview to grasp your federal protections, then check state-level WARN extensions - California, New York, New Jersey, and Illinois each have stronger protections than federal law. Use the company layoff history to research employer patterns before accepting an offer, and the state-level filing tracker to see active WARN notices in your region. For navigating an active layoff, the navigation guide walks through severance review, COBRA timing, and unemployment filing windows. Every notice we publish comes directly from state Department of Labor WARN filings, public records by statute, with vintage stamps on every record.
Source: U.S. Department of Labor / state workforce agencies WARN Act public disclosure reports and state registries compiled by PlainLayoffs · September 2026 Guide figures that cite live portal counts come from this database; legal thresholds and illustrative examples cite public statutes.
How to read the trend
Context beats a single headline count.
- Start with the national monthly series on the homepage. National tracker
- Then drill into industry patterns for the sector you care about. Industry patterns
- Confirm the live rankings before citing a "largest" claim. Rankings hub
WARN filings lag the real-world layoff date; recent months are provisional until state agencies finish posting.
The live counts on this guide are rendered directly from the PlainLayoffs database. Legal thresholds, historical examples, and illustrative figures cited in the guide text come from public statutes and general industry context, not this portal's live database. This guide's WARN Act figures are drawn directly from state filings. See our editorial standards & corrections policy, the methodology behind these numbers, or report a data error. Data current as of September 2026. A WARN filing is a legally required notice, not a judgment of a company's management or financial health; rankings here reflect filed notice volume only.