PitchBook: private-capital and deal data (licensed)

Licensed: not exercised here

PitchBook is a licensed commercial dataset (Morningstar), so it carries no provenance badge: the access path below was not run in this session (no PitchBook credentials were available). The page documents the access route and the gotchas; treat it as unverified until someone exercises it through a licensed account. This is the honest grade under the institute’s Verified discipline.

PitchBook is a deal-level private-capital database. It tracks startups and private companies with their founding year, industry, and location; venture and private-equity funding rounds (date, amount, investors, post-money valuation); funds and their limited partners; and exits through IPO and M&A. It is used in, for example Chen & Ewens for VC fund and startup data and LP commitment information, Hu & Ma for startup characteristics and funding rounds (alongside Crunchbase), and Barkai & Panageas for IPO and M&A exit valuations by founding-year cohort.

• Cost: licensed, subscription. No free tier.
• Vendor: PitchBook Data (a Morningstar company).
• Coverage: private companies, deals, funds, and investors globally, with the deepest coverage in venture capital and private equity.

Access (when licensed)

• Direct from PitchBook. Through the PitchBook platform, data exports, the Excel plug-in, or an API, keyed on PitchBook company, deal, and fund identifiers.
• Possibly via a library or vendor feed. Some institutions reach PitchBook through bundled products; availability is not confirmed here, so check with your library before assuming a given path.
• 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, not verified here.

• Deal coverage is built from public sources plus voluntary input, so it skews to visible deals. Larger, announced, and venture-backed deals are covered better than small or undisclosed private transactions; absence of a deal is not absence of activity.
• Valuations and round terms are often estimated or missing. Post-money valuations and deal amounts can be modeled or undisclosed; treat them as noisy, and check whether a figure is reported or estimated before using it as an outcome.
• History gets restated. Deals, companies, and valuations are added and revised retroactively as information surfaces, so a current pull is not what was knowable at an earlier date. Pin the data vintage and re-pull deliberately for point-in-time designs.
• It overlaps imperfectly with Crunchbase and Preqin. The same company or fund can carry different rounds, amounts, or valuations across vendors. Pick a source of truth per field and document discrepancies; papers that use both (for example Hu & Ma with Crunchbase) reconcile explicitly.
• Industry, location, and founding-year fields carry classification noise. These are PitchBook’s own tags; state how you bucket them so the categories are reproducible.
• Entity mapping is its own step. Join on PitchBook company, deal, and fund identifiers and confirm the parent/subsidiary and ticker mapping before merging with public-market data.

Citation

Cite the product and vendor, e.g.: PitchBook private-capital data, PitchBook Data (Morningstar); data licensed and accessed YYYY-MM-DD. State the data vintage, the module (company, deal, fund, or exit), and whether valuations are reported or estimated.