Experimental Evidence of the Impact of Increasing Auditors' Legal Liability on Firms' New Investments*
Bibliographic record
Abstract
Abstract This laboratory market study examines the potential effect of increasing auditors' liability on firms' new investments. The experimental hypotheses are derived from Shibano's 2000 model, which predicts that an increase in auditors' liability will decrease the frequency of audit failures and may decrease firms' new investments if the liability level is “excessive”. Results from three experimental market settings (with low, medium, and high liability levels) suggest two major conclusions. First, firms' new investments increase significantly when auditors' liability level increases from low to medium, and decrease significantly as the liability level increases from medium to high. This result provides support for the argument that adequate auditor liability is necessary to motivate firms to invest in new projects. Excessive liability, however, may discourage firms from making new investments. Second, the frequency of audit failure decreases insignificantly when auditors' liability increases. These two results have an important policy implication: the benefit of imposing high liability on the auditor (i.e., an insignificant decrease in audit failure) may be more than offset by its cost (i.e., a significant decrease in new investments).
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".