The Association between Audit Quality and Corporate Tax Avoidance. A Bibliometric Review of Literature and Early Evidence on the European Union, from the Perspective of Tax-Related Key Audit Matters Disclosure
Bibliographic record
Abstract
In the circumstances of increasing forms of corporate reporting, the relevance of the financial information is slightly decreasing, as the reporting strategies do not provide evidence of the potential deterioration of reported earnings, but rather try to hide managers’ earnings management practices through various impression management techniques and lower financial transparency. Therefore, the external auditors’ role becomes essential in mitigating the information asymmetry. This article aims to study the association between a quality audit and corporate tax avoidance. The research methodology was based on two essential stages. The first stage consisted of reviewing the specialized literature by applying the bibliometric analysis. In the second stage, we resorted to an exploratory analysis of the KAMs disclosed by European Union firms listed in 2016–2021. The study was carried out based on the information provided by the Web of Science and Audit Analytics databases. In accordance with the obtained results, we emphasize that more attention should be paid to the association between the KAMs disclosed by auditors regarding the extended audit reports and the indication of corporate tax avoidance through different tax planning metrics. At the same time, the study underlines that collections of data on KAMs’ disclosures could help specialists create a common body of knowledge about KAMs and how they should be used as communication tools between auditors, management, and stakeholders (including the state). The contribution of this article consists of providing informational support to the tax authorities to understand the main concerns regarding the business environment so that they can come up with supporting public tax policies that should facilitate the mission of companies to determine the tax burden. In addition, it provides researchers with a starting point to further explore issues related to tax avoidance techniques and the role of a financial auditor in limiting them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.035 | 0.055 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".