(Un)intended Consequences of a Mandatory Dividend Payout Regulation for Earnings Management: Evidence From a Natural Experiment
Bibliographic record
Abstract
We examine the (un)intended consequences of a mandatory dividend payout regulation for firms’ financial reporting choices using a natural experiment in China. Beginning in October 2008, China required firms desiring to raise new equity capital to maintain a cumulative dividend payout ratio of at least 30% over the past 3 years. We find that firms with a cumulative payout ratio from the prior year very close to but slightly lower than the mandated dividend threshold report lower discretionary accruals but do not increase dividends. In addition, this pattern is more pronounced in firms with greater cash flow deficits, firms with faster sales growth, and firms located in regions with lower bank development. We also find that the negative discretionary accruals are concentrated in firms with return on equity well in excess of another regulatory benchmark. In contrast, the only firms that increase dividends in response to the regulation are firms with a positive cumulative payout ratio far below the mandated threshold. Because dividend payout regulations have been suggested as a possible solution to agency problems, our results provide important policy feedback about the effectiveness of such regulations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".