CREAT: Census Research Exploration and Analysis Tool

The Going Public Decision and the Product Market

July 2008

Working Paper Number:

CES-08-20

Abstract

At what point in a firm's life should it go public? How do a firm's ex ante product market characteristics relate to its going public decision? Further, what are the implications of a firm going public on its post-IPO operating and product market performance? In this paper, we answer the above questions by conducting the first large sample study of the going public decisions of U.S. firms in the literature. We use the Longitudinal Research Database (LRD) of the U.S. Census Bureau, which covers the entire universe of private and public U.S. manufacturing firms. Our findings can be summarized as follows. First, a private firm's product market characteristics (market share, competition, capital intensity, cash flow riskiness) significantly affect its likelihood of going public. Second, private firms facing less information asymmetry and those with projects that are cheaper for outsiders to evaluate are more likely to go public (consistent with Chemmanur and Fulghieri (1999)). Third, IPOs of firms occur at the peak of their productivity cycle (consistent with Clementi (2002)): the dynamics of total factor productivity (TFP) and sales growth exhibit an inverted U-shaped pattern. Finally, sales, capital expenditures, and other performance variables exhibit a consistently increasing pattern over the years before and after the IPO. The last two findings are consistent with the widely documented post-IPO operating underperformance of firms being due to the real investment effects of a firm going public, and inconsistent with underperformance being solely due to earnings management immediately prior to the IPO.

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.
:
estimation, market, production, statistical, sale, earnings, corporation, corporate, expenditure, economically, profit, revenue, competitor, public

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.
:
Census of Manufactures, Annual Survey of Manufactures, Internal Revenue Service, Standard Industrial Classification, Longitudinal Research Database, Total Factor Productivity, Securities and Exchange Commission, Cobb-Douglas, Permanent Plant Number, Center for Research in Security Prices, Initial Public Offering, Securities Data Company, Cornell University, Boston Research Data Center, Net Present Value

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