The Going-Public Decision and the Product Market
Bibliographic record
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 article, 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 (total factor productivity [TFP], size, sales growth, market share, industry competitiveness, capital intensity, and cash flow riskiness) significantly affect its likelihood of going public after controlling for its access to private financing (venture capital or bank loans). Second, private firms facing less information asymmetry and those with projects that are cheaper for outsiders to evaluate are more likely to go public. Third, as more firms in an industry go public, the concentration of that industry increases in subsequent years. The above results are robust to controlling for the interactions between various product market and firm-specific variables. Fourth, IPOs of firms occur at the peak of their productivity cycle: the dynamics of TFP and sales growth exhibit an inverted U-shaped pattern, both in our univariate analysis and in our multivariate analysis using firms that remained private throughout as a benchmark. 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 view that the widely documented post-IPO operating underperformance of firms is due to the real investment effects of going public rather than being due to earnings management immediately prior to the IPO.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".