Revelio Labs: workforce and human-capital data (licensed)

Access confirmed via a licensed WRDS session

The keystone query SELECT * FROM revelio.individual_positions LIMIT 1 was run against the live dataset through a licensed WRDS Postgres session on 2026-06-09 and returned a real row (24 columns). The page carries the amber “Access confirmed (licensed)” badge. Reproducing this still requires your own WRDS account and your institution’s Revelio entitlement; the data is licensed, not open.

Revelio Labs builds a firm-level workforce / human-capital panel from public professional profiles and online job postings: headcount, hiring and attrition flows, role and seniority composition, skills, education (including the advanced-degree share of staff), inferred compensation, and sentiment. It maps this to companies and, where listed, to tickers. The Kwan, Liu & Matthies attention paper uses Revelio / LinkedIn data for fund human capital (the advanced-degree share of staff) in its result that more efficient funds employ more highly educated analysts.

• Cost: licensed, subscription. No free tier.
• Vendor: Revelio Labs (workforce intelligence built from aggregated public professional profiles and job postings).
• Coverage: company-level workforce panels, mapped to a Revelio company identifier and, where applicable, to listed-equity tickers.

Access (when licensed)

• Direct from Revelio Labs. Through their data feed, API, or a cloud data share (e.g. Snowflake), keyed on the Revelio company identifier.
• Via WRDS. Revelio Labs is available on WRDS; the keystone query above runs against revelio.individual_positions through a licensed WRDS session. Some institutions also reach it through a direct Revelio feed or cloud share. Check your library’s entitlement.
• Credentials are required either way. Keep them in .env, never hard-coded.

Gotchas (the ones that bite pipelines)

These are the failure modes to expect; they are documented below.

• Coverage skews to white-collar, US, and large firms. The data derives from public professional profiles, which over-represent office roles, the US, and big employers. Headcount levels are estimates, not a census; treat cross-firm level comparisons with care and prefer within-firm changes.
• Profiles are self-reported and inferred. Roles, seniority, and dates are inferred from self-authored profiles, so titles and start/end dates carry noise. Education fields (the advanced-degree share) inherit that noise.
• Historical panels get restated. As the underlying models and profile coverage are reworked, prior periods can be revised. Pin the data vintage and re-pull deliberately rather than mixing vintages.
• Entity mapping is its own step. Join on the Revelio company identifier and confirm the parent/subsidiary and ticker mapping; a single listed parent can span many subsidiary employers, and vice versa.
• Timing is inferred, not point-in-time disclosure. Hiring and attrition are reconstructed from profile changes, which surface with a lag and irregular timing; do not treat a monthly series as a clean as-of snapshot.
• Define the metric explicitly. “Advanced-degree share” and similar measures depend on how degrees and the staff denominator are defined; state the definition so the number is reproducible.

Citation

Cite the product and vendor, e.g.: Revelio Labs workforce data, Revelio Labs; data licensed and accessed YYYY-MM-DD. State the data vintage and the exact metric definition used.