Does XBRL Adoption Constrain Earnings Management? Early Evidence from Mandated U.S. Filers
Bibliographic record
Abstract
ABSTRACT We examine whether the use of eXtensible Business Reporting Language (XBRL) for financial reporting (i.e., interactive data submissions) reduces earnings management during the period of XBRL implementation by the SEC. Using a sample of mandated XBRL filers, we compare the magnitude of absolute discretionary accruals in the XBRL adoption quarters with that in the non‐adopting quarters. We also take advantage of staggered (three‐stage phase‐in) XBRL implementations to perform difference‐in‐differences analyses. Our results show that absolute discretionary accruals decrease significantly from the pre‐ to the post‐XBRL period, suggesting that XBRL adoption constrains earnings management via discretionary accrual choices. Our analyses further reveal that the use of standardized official XBRL elements significantly reduces the levels of discretionary accruals, while the use of customized extension elements does not, suggesting that the former discourages accrual‐based earnings management, while the latter does not. Our results are robust to a variety of sensitivity checks.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".