A Tale of Two Enforcement Venues: Determinants and Consequences of the SEC's Choice of Enforcement Venue After the Dodd-Frank Act
Bibliographic record
Abstract
ABSTRACT The Dodd-Frank Act allows the SEC to choose either an administrative proceeding or a federal court as an enforcement venue for resolving violations of federal securities laws. I examine determinants and consequences of the SEC's choice of enforcement venue after the Dodd-Frank Act. Results show that material cases are 28–35 percent more likely to be assigned to federal courts, and politically connected defendants are about 14 percent more likely to be routed to administrative proceedings. While monetary penalties by venue are statistically indifferent, politically connected defendants in administrative proceedings are associated with lower penalties. Additionally, I find that administrative proceedings process cases 27 times faster than federal courts. Results suggest the SEC's private incentives affect enforcement venue selection and possibly enforcement outcomes. The SEC is more likely to use administrative proceedings when political and economic costs are greater, and use federal courts when political and economic benefits are greater. JEL Classifications: G18; G28; G38; M41; M48; D72.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".