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 591 WARN Act notices so far
According to WARN Act filings submitted to state workforce agencies, 2026 has recorded 591 notices affecting 51,744 workers year-to-date, versus 1,685 notices (145,865 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.
Understanding how to interpret WARN Act mass layoff tracking data requires context that raw numbers alone cannot provide. This guide breaks down the key concepts, common misconceptions, and practical steps for using this data effectively.
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
- 8,798
- WARN notices tracked
- 1,411,142
- Workers on notice
- 6,943
- Employers filing
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 - 277,413 workers across 2,358 notices
277,413 workers
- Washington
Washington - 236,227 workers across 1,056 notices
236,227 workers
- California
California - 197,418 workers across 1,136 notices
197,418 workers
- Indiana
Indiana - 149,707 workers across 994 notices
149,707 workers
- Virginia
Virginia - 123,716 workers across 1,018 notices
123,716 workers
- Oregon 63,395
Oregon - 63,395 workers across 365 notices
63,395 workers
- Maryland 60,723
Maryland - 60,723 workers across 614 notices
60,723 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 225 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) | 591 | 51,744 |
| 2025 | 1,685 | 145,865 |
| 2020 | 2,650 | 455,339 |
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 numbers together
Consider a 5,200-employee tech company announcing a 12% workforce reduction (624 affected). The notice is dated April 1 with separations effective May 31-60 days, satisfying federal WARN. In California, where 380 of the 624 are based, Cal-WARN also requires 60 days plus separate state filing, both met. But in New York, where 95 affected workers are based, NY-WARN requires 90 days. The 60-day notice violates NY law for those 95 workers, exposing the employer to up to 30 days of back pay and benefits per worker, roughly $30,000 to $45,000 per affected worker, or $2.8M to $4.3M aggregate damages just for the New York shortfall. State-specific timing matters more than the federal floor.
Decision-weighted comparison
| Jurisdiction | Employer threshold | Affected threshold | Notice required |
|---|---|---|---|
| Federal WARN | 100+ employees | 50+ at one site (or 33% + 50) | 60 days |
| California (Cal-WARN) | 75+ employees | 50+ in 30 days | 60 days |
| New York | 50+ employees | 25+ (33%) or 250+ | 90 days |
| New Jersey | 100+ employees | 50+ in 30 days | 90 days |
| Illinois | 75+ employees | 25+ (33%) or 250+ | 60 days |
| Tennessee | 50+ employees | 50+ in 3 months | 60 days |
A WARN notice is not a courtesy, it is a federal contract, and the difference between 60 and 90 days of mandated notice is the difference between accepting an offer and litigating one.
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.
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 June 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.
| Publisher | PlainLayoffs |
| Sources | Public state WARN-Act layoff registries |