Bibliographic record
Abstract
Abstract In this article, we examine the effects of Title II of the Jumpstart Our Business Startups (JOBS) Act on the cost and issue size of Rule 144 debts for a sample from 2002 to 2019. We find that after the enactment of the JOBS Act, the average cost (issue size) of the Rule 144 A offers decreases (increases) significantly. The findings are robust after controlling for issue‐, issuer‐, and country‐specific characteristics as well as market conditions. Evidence based on the propensity‐score‐matched sample, including public debt issues, subsample analyses, difference‐in‐differences tests, and alternative event windows, further shows that domestic firms benefit more from the JOBS Act as they have a greater reduction in the cost of debt and increase in the issue size. Overall, our results are consistent with the increased investor base hypothesis and suggest that Title II of the JOBS Act satisfies Congress's goal of making it more cost efficient for Rule 144 A issuers.
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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 | 0.003 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".