Private Litigation Costs and Voluntary Disclosure: Evidence from the <i>Morrison</i> Ruling
Bibliographic record
Abstract
ABSTRACT We examine the causal effect of expected private litigation costs on voluntary disclosure using a natural experiment, the Supreme Court ruling in Morrison v. National Australia Bank. Even though this ruling had no effect on what constituted fraudulent conduct for the purpose of securities litigation, it significantly reduced the expected private litigation costs for foreign cross-listed firms by reducing the pool of potential claimants. It did so by eliminating the right of shareholders who purchased shares on non-U.S. exchanges from seeking compensation in U.S. courts. In the post-Morrison period, we find consistent evidence showing a decrease in voluntary disclosure using analyses that exploit the varying impact of the ruling based on both firm- and country-level attributes. Unlike a number of prior studies, we find that the positive relation between litigation and disclosure does not depend on the direction of the news. JEL Classifications: G15; G18; M41. Data Availability: Data are available from the public sources cited in the text.
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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 | 0.003 | 0.013 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".