CREAT: Census Research Exploration and Analysis Tool

The Disappearing IPO Puzzle: New Insights from Proprietary U.S. Census Data on Private Firms

June 2020

Working Paper Number:

CES-20-20

Abstract

The U.S. equity markets have experienced a remarkable decline in IPOs since 2000, both in terms of smaller IPO volume and entrepreneurial firms' greater tendency to exit through acquisitions rather than IPOs. Using proprietary U.S. Census data on private firms, we conduct a comprehensive analysis of the above two notable trends and provide several new insights. First, we find that the dramatic reduction in U.S. IPOs is not due to a weaker economy that is unable to produce enough 'exit eligible' private firms: in fact, the average total factor productivity (TFP) of private firms is slightly higher post-2000 compared to pre-2000. Second, we do not find evidence supporting the conventional wisdom that the disappearing IPO puzzle is mainly driven by the decline in IPO propensity among small private firms. Third, we do not find a significant change in the characteristics of private firms exiting through acquisitions from pre- to post-2000. Fourth, the decline in IPO propensity persists even after we account for the changing characteristics of private firms over time. Fifth, we show that the difference in TFP between IPO firms and acquired firms (and between IPO firms and firms remaining private) went up considerably post-2000 compared to pre-2000. Finally, venture-capital-backed (VC-backed) IPO firms have significantly lower postexit long-term TFP than matched VC-backed private firms in the post-2000 era relative to the pre- 2000 era, while this pattern is absent among IPO and matched private firms without VC backing. Overall, our results strongly support the explanations based on standalone public firms' greater sensitivity to product market competition and entrepreneurial firms' access to more abundant private equity financing in the post-2000 era. We find mixed evidence regarding the explanations based on the smaller net financial benefits of being standalone public firms or the increased need for confidentiality after 2000.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
investment, market, company, sale, enterprise, merger, takeover, acquisition, venture, entrepreneurial, entrepreneur, investor, firms size, firms grow, acquirer, profit, stock, equity

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

The model is able to label words and phrases by part-of-speech, including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are identified to contain references to specific institutions, datasets, and other organizations.
:
Standard Statistical Establishment List, Census of Manufactures, Longitudinal Research Database, Annual Survey of Manufactures, Center for Economic Studies, Ordinary Least Squares, Total Factor Productivity, Cobb-Douglas, Securities and Exchange Commission, Longitudinal Business Database, Initial Public Offering, Securities Data Company, Census of Manufacturing Firms, North American Industry Classification System, Business Register, Herfindahl Hirschman Index, Herfindahl-Hirschman

Similar Working Papers Similarity between working papers are determined by an unsupervised neural network model know as Doc2Vec.

Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the capture of semantic meaning in a way that relates to the context of words within the document. The model learns to associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as document classification, clustering, and similarity detection by preserving the order and structure of words. The document vectors are compared using cosine similarity/distance to determine the most similar working papers. Papers identified with 🔥 are in the top 20% of similarity.

The 10 most similar working papers to the working paper 'The Disappearing IPO Puzzle: New Insights from Proprietary U.S. Census Data on Private Firms' are listed below in order of similarity.