The U.S. Left Behind: The Rise of IPO Activity Around the World
Bibliographic record
Abstract
During the past two decades, there has been a dramatic change in IPO activity around the world.Though vibrant IPO activity, attributed to better institutions and governance, used to be a strength of the U.S., it no longer is.IPO activity in the U.S. has fallen compared to the rest of the world and U.S. firms go public less than expected based on the economic importance of the U.S. In the early 1990s, the declining U.S. IPO share was due to the extraordinary growth of IPOs in foreign countries; in the 2000s, however, it is due to higher IPO activity abroad combined with lower IPO activity in the U.S. Global IPOs, which are IPOs in which some of the proceeds are raised outside the firm's home country, play a critical role in the increase in IPO activity outside the U.S. The quality of a country's institutions is positively related to its domestic IPO activity and negatively related to its global IPO activity.However, home country institutions are more important in explaining IPO activity in the 1990s than in the 2000s.The evidence is consistent with the view that access to global markets helps firms overcome the obstacles of poor institutions.Finally, we show that the dynamics of global IPO activity and country-level IPO activity are strongly affected by global factors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".