💼 BLS Occupation Projections Scraper

🚀 Export U.S. BLS 2024-2034 occupational projections in seconds. SOC code, occupation title, 2024 and 2034 employment, numeric and percent change, annual openings, median wage, typical education, and work experience - straight to CSV, Excel, JSON, or XML.

The BLS Occupation Projections Scraper turns the public Bureau of Labor Statistics Occupational Projections and Worker Characteristics table into a clean, structured dataset. It hits the same static HTML page the BLS publishes and parses every row - Summary, Major, Minor, Broad, and Detailed SOC levels - into a flat record per occupation.

Coverage spans the complete 2024-2034 employment projection: all 1,100+ occupations the BLS National Employment Matrix tracks, with employment counts (in thousands), growth numbers (numeric and percent), annual openings, median annual wage (USD), and the typical education and experience requirements for entry.

🎯 Target Audience	💡 Primary Use Cases
🎓 Career counselors	Recommend high-growth, high-wage occupations
📊 Workforce analysts	Track decade-out projections by SOC sector
🏢 HR / talent teams	Plan hiring against projected supply changes
📰 Journalists	Cite official BLS figures for occupation trend pieces
🤖 AI / ML teams	Train a career-recommendation engine on official numbers
🏛️ Policy researchers	Model labor market shifts by sector

📋 What the BLS Occupation Projections Scraper does

• Fetches the public BLS Occupational Projections HTML table.
• Parses every row (Summary, Major, Minor, Broad, and Detailed levels).
• Flattens each occupation into a 16-field record with occupationTitle first and error last.
• Applies optional filters: occupationType, minMedianWage, minEmploymentChangePercent.
• Exports as CSV, Excel, JSON, JSONL, XML, RSS, or HTML.

💡 Why it matters: the BLS publishes the projections as a 70-column wide HTML table that is painful to copy by hand. This actor flattens it into a structured dataset you can query, filter, and join.

📊 Data fields

Each record includes: annualOpenings, employment2024, employment2034, employmentChangeNumeric, employmentChangePercent, medianAnnualWage, occupationTitle, occupationType, onJobTraining, oohUrl, percentSelfEmployed, results, scrapedAt, socCode, sourceUrl, typicalEducation, workExperience. These field names come straight from the actor's dataset schema, so what you see here is what lands in your dataset.

⚠️ Good to Know: employment figures (employment2024, employment2034, employmentChangeNumeric, annualOpenings) are in thousands. medianAnnualWage is in USD. A — value in the source table is normalised to null.

🚀 How to use

1. Sign up. Create a free Apify account w/ $5 credit.
2. Open the BLS Occupation Projections Scraper page.
3. Set inputs. Set maxItems and any filters.
4. Run. Click Start. Free users get 10 items, paid users up to 1,000,000.
5. Export. Download the dataset as CSV, Excel, JSON, JSONL, XML, RSS, or HTML.

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⚠️ Disclaimer: this is an independent tool, not affiliated with the U.S. Bureau of Labor Statistics or the Department of Labor. Only publicly available data from bls.gov is collected.

🆘 Need Help?

If you hit a bug, have questions about setup, or need a scraper we haven't built yet, open our contact form or write to parseforge@protonmail.com. We also take on paid custom data projects.

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