Corporate Governance and Earnings Management: Evidence from Shareholder Proposals*
Bibliographic record
Abstract
ABSTRACT We examine the causal effects of corporate governance on earnings management using shareholder‐sponsored proposals that pass or fail by a small margin of votes in annual shareholder meetings. This setting provides a causal estimate that overcomes concerns of endogeneity. Specifically, compared with firms whose shareholder proposals fall just short of a majority threshold, firms whose shareholder proposals narrowly pass have similar characteristics but a discretely higher likelihood of implementing improvements in governance. As such, we expect that firms whose shareholder proposals pass the threshold by a small margin exhibit a significantly lower level of earnings management. Employing a regression discontinuity design, we find results that support our expectation based on the propensity to just meet or beat analysts' forecasts by one cent as a proxy for earnings management. In addition, we show that the results are driven by governance changes that increase directors' monitoring. Our results are robust to using discretionary accruals as an alternative measure of earnings management. Collectively, the results suggest that improvements in corporate governance curtail earnings management, and support the underlying premise of regulators that improvements in corporate governance would improve financial reporting.
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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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".