Corporate Political Activities and the <scp>SEC</scp>'s Oversight Role in the <scp>IPO</scp> Process
Bibliographic record
Abstract
Abstract We study how a regulator (Securities and Exchanges Commission; SEC) responds to IPOs that have a higher political profile. We find that IPOs with issuers (intermediaries) that actively pursue political strategies receive more (less) SEC comment letters than IPOs without such actors. Cross‐sectional analysis reveals that the IPO's political environment moderates the relationship between social pressure for more corporate transparency and SEC scrutiny. Additional tests indicate that the political activities of issuers (intermediaries) contribute to a less (more) efficient IPO process. Overall, our findings suggest that politically active intermediaries have stronger incentives to accurately portray the IPO financial reporting environment than politically active issuers because they have greater reputational and political capital at stake; quite simply, the former have more to lose. We draw out the implications for theory, in terms of agency and reputation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".