Upload README.md

README.md

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----
-license: cc-by-4.0
----
+---
+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
+```