Motivating Managers to Invest in Accounting Quality: The Role of Conservative Accounting*
Bibliographic record
Abstract
ABSTRACT Although internal control over financial reporting has gained increasing regulatory attention, its enforcement is far from perfect; thus, firm‐specific incentives to management become important to increase the quality of financial reports. We study how owners can motivate managers to invest in accounting quality even though it is costly to the managers. Using an agency model, we establish that a sufficiently conservative accounting system (which understates performance) is necessary to induce a manager to invest in accounting quality, and more conservatism increases this investment. The reason is that higher accounting quality mitigates the expected reduction of the manager's compensation from conservatively measured performance. Higher accounting quality makes the performance measure more precise, and the owner optimally lowers incentives, even though that entails some loss of productivity. In total, more conservatism increases both firm value and accounting quality. Our findings suggest that striving for neutral accounting can counteract incentives to improve accounting quality, and they provide support to using conservatism as a metric of financial reporting quality in empirical studies.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".