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US Pay-Transparency Compliance Checker

👁 Python 3.10+
👁 Source

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.

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.

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.