Does Attestation of the effectiveness of Internal Control over Financial Reporting Discourage Earnings Management? Evidence from China
Bibliographic record
Abstract
To improve financial reporting quality, the Chinese government issued the Basic Standard for Enterprise Internal Control in 2008 and other related guidelines/regulations in the following years (hereafter China SOX). The scope of China SOX is broader but similar to Section 404 of the Sarbanes-Oxley Act (SOX) in the U.S. Formal adoptions of China SOX requires management and external auditor’s report on the effectiveness of internal control over financial reporting (ICFR). A company’s ICFR, if effective, should provide reasonable assurance that the company’s financial statements are reliable and prepared in accordance with the applicable accounting standards. The purpose of this study is to investigate whether China external auditor attestation of ICFR discourage earnings management, an indicator of financial reporting quality. By analyzing a sample of Chinese public firms during 2011 to 2013, we find that: (1) Chinese firms that disclose audited ICFR reports exhibit lower earnings management than firms that do not; (2) Chinese firms that are mandated to disclose audited ICFR reports exhibit lower earnings management than firms that voluntarily disclose audited ICFR reports. Our empirical results seem to suggest that attestation of the effectiveness of ICFR discourages earnings management and therefore improve financial reporting quality.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| 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".