README.md

---
license: cc-by-4.0
language:
  - en
size_categories:
  - 100K<n<1M
task_categories:
  - tabular-classification
  - tabular-regression
tags:
  - government
  - regulation
  - labor
  - wage-theft
  - whd
  - dol
  - compliance
  - federal-data
  - public-records
pretty_name: WHD Wage Theft Enforcement Actions by Employer
---

# WHD Wage Theft Enforcement Actions by Employer

40,000+ DOL Wage and Hour Division enforcement cases aggregated to the employer level. 323,514 US employers, $4.6 billion in back wages, 5.2 million affected employees.

## What this dataset is

What does federal wage theft enforcement actually look like in aggregate? This dataset answers that question.

The Department of Labor's Wage and Hour Division (WHD) publishes enforcement data at the case level, which makes it hard to see the employer-level picture. This dataset aggregates 350,829 individual enforcement cases into a single row per US employer — making it trivial to identify repeat offenders, calculate cumulative back wages, and filter by geography or industry.

323,514 US employers. $4.6 billion in total back wages owed. 5.2 million affected employees.

Source: [fastdol.com](https://fastdol.com).

## What's in the data

Each row represents a single US employer with one or more WHD enforcement cases.

- **Employer identity**: name, city, state, ZIP, NAICS classification, parent company (where known)
- **WHD enforcement metrics**: case count, total back wages owed, employees affected, back wages per employee
- **OSHA cross-reference**: violation count and penalty totals where applicable

## Findings to explore

**Repeat offenders are concentrated.** 20,880 employers have 2 or more WHD cases. These repeat offenders account for $1.0 billion in back wages — roughly 22% of all back wages in the dataset. Wage theft enforcement is not evenly distributed across employers.

**Cross-agency overlap.** Employers with multiple WHD cases are meaningfully more likely to also have OSHA enforcement records than single-case employers. Two independent federal regulatory systems — and there's a measurable correlation. The kind of cross-agency pattern this data is built to surface.

**Industry concentration.** Construction, food services, and admin/waste management dominate the repeat-offender population. These are sectors where WHD has historically focused enforcement and where the workforce is more vulnerable.

**State-level concentration.** California and Texas dominate by raw counts (large workforce, large enforcement footprint). Sorting by back wages per employer reveals where enforcement intensity is highest relative to employer count.

## Suggested use cases

- Investigative journalism — identify repeat wage theft offenders by city, state, or industry
- Plaintiffs' attorneys — research defendant enforcement history
- Labor economics research — aggregate patterns in WHD enforcement
- ESG and supply-chain due diligence — screen vendors for wage compliance history
- Policy analysis — measure enforcement intensity across states and industries
- ML feature engineering — wage compliance as a model input

## Loading the data

```python
import pandas as pd
df = pd.read_csv('wage_theft_whd_enforcement_DOL.csv',
                 dtype={'naics_code': str})
```

## Methodology

Federal WHD enforcement data aggregated from the DOL WHISARD database. OSHA cross-reference uses normalized employer name + state + ZIP matching against the OSHA IMIS database. Entity resolution: when multiple WHD cases share the same normalized employer identity, they're aggregated to a single row with summed metrics.

Most NAICS codes and parent-company linkages are sparse — source agency records don't always include clean industry codes or parent identification. The OSHA cross-reference column is populated for every row but most employers (~95%) have zero OSHA records, since most workplace enforcement happens at one agency rather than several.

Counts reflect what WHD investigated and recorded.

Findings reported descriptively. Aggregate analysis of public federal enforcement data; not a statement about any specific employer's current operations or future risk. Not legal, financial, or underwriting advice.

## About FastDOL

FastDOL aggregates federal enforcement records from 15 US agencies into queryable employer profiles with entity resolution.

- **Source**: [fastdol.com](https://fastdol.com)
- **Methodology**: [fastdol.com/methodology](https://fastdol.com/methodology)
- **API**: [fastdol.com/docs](https://fastdol.com/docs) — free tier available
- **Mirror on Kaggle**: [WHD Wage Theft Enforcement Actions by Employer](https://www.kaggle.com/datasets/benturneroffice365/whd-wage-theft-enforcement-actions-by-employer)

## Citation

```
FastDOL (2026), WHD Wage Theft Enforcement Actions by Employer, fastdol.com
```