Meta-analysis and the Accounting Literature: The Case of Audit Committee Independence and Financial Reporting Quality
Bibliographic record
Abstract
We conduct a meta-analysis (MA) of the association between audit committee (AC) independence and financial reporting quality (FRQ). Although we cannot reliably aggregate results across studies in a statistical sense because of inconsistencies in defining FRQ and the absence of replication studies, quantitative review techniques yield three conclusions: (1) The use of different FRQ measures in the AC independence literature explains about half of the variation in results across studies. (2) Audit committees are more effective at enhancing audit quality (e.g. through averting going-concern reports and auditor resignations) than they are at fostering financial statement quality (e.g. by making high quality accruals and avoiding restatements). AC independence can even reduce apparent financial statement quality by identifying the need for restatements and remedial, abnormal accruals. (3) Financial statement quality and audit quality are complementary contributors to FRQ. The statistical and methodological difficulties we encounter lead us to posit that the dearth of MA studies in accounting and auditing stems from similar difficulties in applying MA to other topics. We present evidence consistent with publication biases and perverse researcher incentives being responsible for the difficulties.
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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 | 0.296 | 0.536 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".