Economic Policy Uncertainty, Corporate Risk-Taking and Abnormal Audit Fees
Bibliographic record
Abstract
As a result of economic imbalances, global turmoil, and catastrophic global health events, economic policy uncertainty is rising across countries, and corporate risk taking is heterogeneous, which can affect auditors’ decisions. This paper explores the impact of economic policy uncertainty on abnormal audit fees based on a corporate risk-taking perspective, using A-share listed companies in China from 2007-2019 as a research sample. According to the research, the level of abnormal audit fees increases as economic policy uncertainty increases, and corporate risk-taking worsens the correlation. Further research shows that higher economic policy uncertainty leads auditors to increase additional inputs and charge a higher compensation for audit risks, resulting in a larger positive and negative abnormal audit fee. Additionally, the positive association between economic policy uncertainty and abnormal audit fees is present in non-state-owned enterprises, while it is not significant in state-owned enterprises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".