15284: The Illusion of Precision: A Reexamination of VC Investment Data

Commercial equity financing transaction databases, such as VentureXpert, Pitchbook, and Crunchbase, are widely utilized in entrepreneurship and strategy research. These databases provide dependent and independent variables, including fact-of-financing, transaction attributes (e.g., total funding), and measures based on these attributes (e.g., prior syndication ties). However, these datasets were created for practitioners, not scholars, and their design reflects judgments suited to practitioner use cases. Collecting information on equity investments in private companies is challenging. Unlike public companies, private firms have minimal disclosure obligations. In the US, these are limited to SEC Regulation D filings, "Blue Sky" filings, and corporate charter amendments. Additional data often comes from self-reporting by insiders, investors, and limited partners, which is of uncertain quality. Commercial databases suffer from measurement error and selection biases despite their polished appearance. These biases affect which transactions are included and how transaction facts are recorded. Temporal biases also arise when follow-on financings lead to retroactive updates of prior transaction details. This Workshop has three goals: (1) to explore primary sources of equity investment data and their strengths and weaknesses; (2) to discuss biases in commercial databases and strategies to leverage primary sources more effectively; and (3) to use interactive roundtables and plenary discussions to share techniques for disclosing, quantifying, and adjusting for bias, as well as identifying alternative data sources. A key focus will be on using AI and Large Language Models (LLMs), such as ChatGPT, to classify documents and extract information without relying on the judgments of database compilers.