US Pay-Transparency Compliance Checker

Pair this with any job-scraper actor. Scrape postings from LinkedIn, Indeed, Glassdoor, or company career pages, then feed the results here to flag which postings violate US state pay-transparency laws. Covers 9 states including Maine LD 54 (effective 2026-07-29) — CA, NY, CO, WA, MA, IL, NJ, OR, ME.

You get per-(posting × state) reports with the exact violation text, regulatory notes, and effective-date awareness. ATSes, recruiters, market-research teams, and comp analysts use it as the compliance layer on top of their existing scraping pipeline.

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Works seamlessly with

Run any of these scrapers first, then chain their dataset into this actor:

• misceres/indeed-scraper — Indeed job postings
• curious_coder/indeed-scraper — alternate Indeed scraper with employer details
• bebity/linkedin-jobs-scraper — LinkedIn job listings
• bebity/glassdoor-jobs-scraper — Glassdoor postings
• Or any other actor that returns job-posting JSON

The expected input shape is documented below. If the scraper's field names differ from ours, a one-step transform (Apify Tasks or webhook) maps jobTitle → title, salaryMin → salary_min, etc.

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What gets checked

For each (posting, state) pair:

• Numeric salary range present (CA, NY, CO, WA, MA, IL, OR, ME require it; NJ is on-request only)
• Range plausibility (inverted min > max; ≥10× spread flagged as bad-faith disclosure)
• No vague-pay phrases alongside the range ("competitive", "DOE", "negotiable") — several state DOLs flag these as non-compliant
• Benefits description present (CO, WA, IL require it)
• Employer size threshold (e.g. CA 15+, MA 25+; smaller employers exempt)
• Effective-date awareness (Maine LD 54 returns soft-pass before 2026-07-29 with a clear note)

Input

{
  "postings": [
    {
      "title": "Senior Software Engineer",
      "description": "We are hiring a senior engineer.",
      "salary_min": 140000,
      "salary_max": 180000,
      "benefits": "Medical, dental, vision, 401k 4% match.",
      "employer_size": 250
    }
  ],
  "states": ["CA", "CO", "NY", "WA"]
}

employer_size is optional — when omitted, the checker assumes the posting is from an employer above the threshold (most postings are from larger companies). For small-employer accuracy, fill it in.

Output (one dataset item per posting × state)

A compliant posting:

{
  "posting_title": "Senior Software Engineer",
  "state": "CA",
  "passes": true,
  "violations": [],
  "notes": [],
  "rule_effective": true
}

A non-compliant posting (Colorado requires both salary AND benefits):

{
  "posting_title": "Marketing Manager",
  "state": "CO",
  "passes": false,
  "violations": [
    "Colorado requires a numeric salary range in the posting (found min=None, max=None).",
    "Colorado requires a benefits description in the posting."
  ],
  "notes": ["Applies to remote jobs that could be performed in CO."],
  "rule_effective": true
}

Pricing

Currently free. You only pay Apify's standard platform compute (typically a fraction of a cent per posting checked). Per-result creator pricing arrives once usage proves demand.

Local development

python -m venv .venv
# Windows: .venv\Scripts\activate
# Mac/Linux: source .venv/bin/activate

pip install -e ".[dev]"
pytest                       # 41 tests, ~50ms
python main.py --local       # run the entrypoint without Apify SDK

Sister projects

This actor shares its compliance rule data with two other surfaces of the same product:

• hr-compliance-mcp — open-source MCP server for Claude / Cursor / Windsurf. 19 tools covering wage, overtime, leave, transparency, discrimination, and more across the same 9 states.
• hr-compliance-site — interactive demo + REST API waitlist for teams that need HTTP access without MCP.

License

LICENSE — source at github.com/Mahender22/pay-transparency-apify.