State Liability Regimes within the United States and Auditor Reporting
Bibliographic record
Abstract
ABSTRACT We examine how state liability regimes within the United States affect auditor reporting decisions. We exploit variation across state-level common law in two aspects of auditor liability: the extent to which auditors can be held liable by third parties for negligence, and rules for apportioning liability across multiple defendants. We find that auditors are more likely to issue a modified going-concern (GC) report to financially distressed clients from high-liability states than to those from low-liability states. We sharpen inferences using a natural experiment that examines the causal effects of two exogenous shocks to auditor third-party liability standards, which dramatically restricted auditors' liability in New Jersey in 1995 and in California in 1992. Results from difference-in-differences tests imply that auditors' propensity to issue a modified opinion for client firms in New Jersey and California decreases significantly after the decline in auditors' litigation exposure, relative to control firms from other jurisdictions. These findings add to our understanding of how litigation risk affects auditor behavior and highlight an important source of variation in litigation risk within the U.S. that has seldom been studied to date.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".