Navigating the Storm: How Economic Uncertainty Shapes Audit Quality in BRICS Nations Amid CEO Power Dynamics
Bibliographic record
Abstract
This study investigates the association between economic uncertainty and audit quality in the BRICS nations, examining both input-based (e.g., audit fees, auditor tenure) and output-based (e.g., restatements, total accruals) measures of audit quality. Utilizing a dataset of 83,511 firm-year observations from 1995–2022, it reveals a significant negative impact of economic uncertainty on audit quality. Additionally, the research explores the moderating role of CEO power, employing principal component analysis to merge various indicators of CEO influence. Findings indicate that powerful CEOs can mitigate the adverse effects of economic uncertainty on audit quality, suggesting a U-shaped relationship between CEO power and audit quality. Methodologically robust, employing techniques like two-stage least squares (2SLS) and two-stage system generalized method of moments (system GMM) to address endogeneity, the study offers a comprehensive analysis of audit quality in the context of economic fluctuations and corporate governance, contributing significantly to the understanding of these dynamics in emerging economies, particularly in the diverse and influential BRICS nations. This study’s findings have significant implications for stakeholders and policymakers, providing insights that can inform policy decisions and enhance corporate governance frameworks.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".