The Bureau of Labor Statistics publishes the Occupational Employment and Wage Statistics survey annually. OEWS covers wage data for over 800 occupations across every US state, metropolitan area, and industry. It is the authoritative source for benchmarking compensation, analyzing labor market conditions, and modeling workforce costs. The BLS website makes individual queries easy but bulk extraction requires programmatic access.
Why automate this?
Compensation analysts benchmark salaries against BLS percentiles for annual review cycles. HR teams building job descriptions need market rates by occupation and geography. Workforce development agencies identify high-wage occupations in their region to guide training investments. Economists studying wage inequality pull OEWS data across occupations and years. Financial models for labor-intensive businesses need defensible wage assumptions by role and location. The BLS data portal handles one query at a time well, but building a full compensation dataset requires automated extraction across multiple occupations and geographies.
What data you get
The BLS OEWS Wage and Salary scraper returns structured records including:
- SOC occupation code and title
- Geographic area (national, state, MSA, nonmetro)
- Employment estimate
- Hourly and annual mean wage
- Percentile wages (10th, 25th, 50th, 75th, 90th)
- Industry cross-tabulation when available
- Survey year
You can query by occupation code or title, geographic area, and survey year. Results are returned as JSON.
How it works
The scraper queries the BLS OEWS data API and tables. It translates occupation names and area names into the correct SOC and area codes, handles the response format, and returns normalized records. You do not need to manage SOC code lookups or BLS geographic area identifiers.
Common use cases
Compensation teams pull percentile wages for a set of occupations across target hiring markets to build salary band recommendations. Workforce boards pull employment and wage data for target occupations in their region to prioritize training programs. Labor economists extract multi-year wage trends for specific occupations to study earnings dynamics. Staffing firms build internal rate benchmarks for job families they place frequently.
Getting started
The actor runs on Apify. Provide an occupation name or SOC code, select a geographic scope, and run. No BLS registration required. Output is structured JSON ready for compensation models, dashboards, or analysis.
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