The Joint Impact of Accountability and Transparency on Managers’ Reporting Choices and Owners’ Reaction to Those Choices
Bibliographic record
Abstract
We report the results of an experiment designed to investigate the fundamental conflict of interest between managers and owners in a financial reporting setting. In our setting, owners seek accurate reports of financial performance whereas managers have incentives to distort performance reports in a self-serving fashion. Regulatory responses to such conflicts often call for improved disclosure, including more accountability and transparency (e.g., Sarbanes-Oxley Act and Dodd-Frank Act). We use the term accountability to imply answerability — wherein managers are required to reconcile the difference between reported and actual performance. We predict and find that when managers’ incentives are transparently disclosed, accountability does not rein in managers’ opportunistic reporting. By comparison, when managers’ incentives are less transparently disclosed (opaque), accountability dampens managers’ propensity to misreport. However, this reduction in opportunistic reporting due to accountability comes about because managers offset higher reporting bias in compensation periods with lower reporting bias in other periods. Therefore, not only are the benefits of accountability restricted to the setting where managers’ incentives are opaque, but the reduced reporting bias might arise due to window-dressing. Although managers seem to care enough about accountability to engage in window-dressing, financial incentives seem to dominate accountability, at least in our setting. We also find that managers’ payoffs are higher when their incentives are opaque, but owners’ payoffs are invariant regardless of whether incentives are transparent or opaque. Our analyses suggest that owners may be relying on accountability to curb opportunistic reporting by managers — a reliance that may be misplaced. Our findings have implications for regulatory responses aimed at addressing conflicts of interest.
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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 | 0.026 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".