Employer Red Flags in Government Data

Why This Matters

Job seekers comparing offers, researchers building employer profiles, and journalists tracking industry trends all benefit from a structured way to read OSHA, DOL, and WARN data. The challenge is that any of these datasets, looked at in isolation, can mislead: a single 2020 WARN notice during the pandemic says little about a company's 2026 stability, and one OSHA citation in a thousand-employee operation may reflect a single supervisor rather than a culture. Pattern-level reading — not single-event reactions — is what makes this data useful.

The challenge is that government data was 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 from PlainEmployers.

This guide walks through the four red-flag patterns that show up most often in OSHA, DOL/ETA, and state WARN registries — clustered WARN notices, DART rates persistently above the NAICS industry mean, falling H-1B prevailing-wage tiers over multiple filing cycles, and repeat-citation OSHA inspection history — and what each looks like in the data on PlainEmployers when you're evaluating a real offer.

Key Concepts to Understand

What OSHA captures: OSHA's Injury Tracking Application receives annual injury and illness summaries from employers with 250+ workers in most industries, plus smaller employers in select high-hazard NAICS codes. The DART rate (Days Away, Restricted, or Transferred) measures lost-worktime cases per 100 full-time workers and is the most comparable cross-employer safety indicator we have at scale. Enforcement records — citations, penalties, abatement deadlines — sit in a separate OSHA database (OIS) and reveal whether OSHA inspected the employer and what they found.

What WARN captures: State workforce agencies publish 60-day advance notices required of employers with 100+ employees before mass layoffs or plant closures. Coverage varies by state — California, New York, and Illinois publish decade-plus histories; some states post only the current year. A WARN notice always names the employer, location, affected-worker count, event type (layoff, closure, relocation), and effective date. What WARN does NOT capture: smaller layoffs below the 100-worker threshold, attrition, or voluntary separations.

What H-1B Labor Condition Applications capture: Every employer sponsoring an H-1B visa worker files an LCA with DOL/ETA disclosing the offered wage, prevailing wage tier (Level 1-4), worksite location, and job title. The filed wage is binding — if the employer pays less than the LCA-stated amount, that's a DOL violation. A multi-year decline in filed wage tiers for the same role at the same employer is one of the harder-to-mask red flags, because tier downgrades require DOL re-filing.

Common Misconceptions

The most common mistake when reading OSHA citation history is treating a single recordable incident as a culture indicator. OSHA Form 300 logs capture every recordable case — including injuries from common slips, ergonomic strain, or routine handling tasks that happen at every employer in a given NAICS code. The signal worth weighing is rate and recurrence: how does this employer's DART rate compare against its three-digit NAICS industry mean, and has the rate trended up or down across the last three reporting years?

Another common mistake is assuming more recent data is always more relevant. Government data typically has a reporting lag of 12-24 months. The most recent available figures may describe conditions that have already changed, particularly in rapidly evolving sectors or regions. Always note the data vintage when making time-sensitive decisions.

A third misconception is that government data is always complete. In reality, reporting thresholds, voluntary participation rates, and processing delays mean that every dataset has gaps. PlainEmployers presents data as reported by source agencies, noting gaps where they are known. Absence of data does not mean absence of activity.

Practical Steps for Using the Data

Step 1 — Start with the big picture. Before drilling into specific records, check the broad trends on PlainEmployers. 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, compare it against similar entities rather than the national average. Geographic, industry, and size differences create natural variation that makes broad comparisons potentially misleading.

Step 3 — Check the source documentation. Every data point on PlainEmployers traces back to a government source. When the stakes are high — career decisions, policy analysis, research publications — verify critical figures against the primary source. We provide source attribution on our data pages and about page.

Step 4 — Apply judgment that data cannot provide. Data is a starting point, not a final answer. The best decisions combine quantitative data with qualitative context — local knowledge, expert consultation, and direct observation. Use PlainEmployers data to narrow your focus and inform your questions, not to replace professional judgment or lived experience.

Frequently Asked Questions

What data does PlainEmployers use?

PlainEmployers uses data from DOL H-1B filings, OSHA safety records, and WARN Act notices. All data comes from public sources and is processed through our pipeline for searchability and analysis.

How often is the data updated?

We update our database as new data becomes available from source agencies. Frequency depends on the source release schedule, which varies from monthly to annually depending on the dataset.

How should I interpret the data?

Always compare within appropriate reference groups. Aggregate statistics describe populations, not individual cases. See our full guide library for detailed interpretation frameworks.

Is PlainEmployers free to use?

Yes. PlainEmployers is completely free, requires no account, and is supported by non-intrusive advertising. We believe public data should be freely accessible to everyone.