Universal demand laws and the monitoring demand for accounting conservatism
Bibliographic record
Abstract
Abstract While prior research holds the consensus that accounting conservatism can serve as an effective monitoring device, it is not clear whether shareholders can successfully enforce managers’ adherence to accounting conservatism when directors fail to fulfill their fiduciary duties. We attempt to answer the question by exploiting staggered enactments of the universal demand (UD) laws in 23 US states. UD laws raise procedural hurdles for shareholders to file derivative lawsuits against managers and directors who allegedly breach their fiduciary duties. For firms incorporated in states that adopt UD laws, restrictions on shareholder litigation rights weaken directors’ incentives to monitor managers. We predict and find a decrease in conditional conservatism following the enactment of UD laws. The decline in conditional conservatism exists only for firms with low institutional ownership, low external equity dependence, or high ex‐ante derivative lawsuit risk. The main result is attributable to both the direct channel (through restriction of shareholder litigation rights) and the indirect channel (through the deployment of management‐friendly governance provisions). Our findings suggest that shareholders cannot successfully demand accounting conservatism when directors lack the incentives to monitor managers.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".