Accounting Standards, Reporting Incentives, and Earnings Management
Bibliographic record
Abstract
In this study, we examine which factor, firms’ accounting standards or firms’ reporting incentives, has a greater impact on firms’ earnings management behavior. To answer this question, we utilize unique hand-collected data that consists of foreign firms cross-listed in the U.S. using U.S. GAAP. This interesting setting allows us to control for differing accounting standards and external monitoring from the SEC between foreign firms and their U.S. domestic counterparts. Therefore, if there is any observed difference in the level of firms’ earnings management, that difference can be mainly attributed to firms’ reporting incentives rather than firms’ accounting standards. Our findings suggest that cross-listed foreign firms using U.S. GAAP exhibit more accruals-based and real activities earnings management relative to domestic firms. The results suggest that accounting standards, regulations, and enforcement is not enough to eliminate opportunistic reporting behavior. Firm incentives will still impact the magnitude of earnings management. This finding is particularly important given the hot debate regarding whether the U.S should adopt IFRS or not. No matter what accounting standards firms choose, U.S GAAP or IFRS, firms’ earnings quality can still vary with differing reporting incentives.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".