Does tax enforcement inform auditors' risk assessment? Evidence from key audit matters
Bibliographic record
Abstract
Abstract International standards encourage auditors to consider regulatory factors and external parties during the risk assessment process. One such external party is the taxation authority, which monitors corporate conduct and uses the threat of tax audits to constrain managerial opportunism. This study examines whether tax enforcement influences auditors' perception of the risk of material misstatement on their engagements. Using a sample of companies listed on European exchanges, we assess the strength of tax enforcement at the country level based on the number of full‐time equivalent (FTE) employees in the tax audit and verification function relative to the size of the economy. Since auditors of these European listed companies must publicly disclose key audit matters (KAMs), we use the number of KAMs to capture auditors' perceptions of client‐level misstatement risk. Our results indicate that auditors report fewer KAMs in the presence of more FTEs in the tax audit and verification function. In cross‐sectional tests, we find that this negative association is stronger in settings where auditors are more inclined to incorporate the monitoring potential of the tax authority into their risk assessment, such as in countries with high book‐tax conformity and for auditors with greater exposure to complex tax issues. These findings offer new insights into the role of tax enforcement in auditors' decision‐making and have timely implications for accounting regulators and academics studying the determinants of KAMs.
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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 | 0.022 | 0.189 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".