Audit committee characteristics and tax aggressiveness
Bibliographic record
Abstract
Purpose This study aims to analyze the relationship between a company’s use of aggressive tax planning and several audit committee members’ characteristics, namely, independence, expertise, diligence and gender diversity. Design/methodology/approach This paper is an empirical research using archival data from 289 Canadian listed companies for the 2011-2015 period. Findings The authors find that measures of expertise and diligence are significantly related to tax aggressiveness. Financial expertise and tenure on the audit committee play an important role in constraining tax aggressiveness, as does having a larger audit committee. Research limitations/implications One limitation – and an area for future research – is that the effects of the audit committee members’ relationships with managers of the firms were not investigated. Practical implications Knowledge of audit committee characteristics may send a signal to shareholders, investors and tax agencies regarding the company’s potential risk with respect to aggressive tax planning. The analysis provides useful insights for board governance committees when determining the profile of persons to nominate for board positions and committees. In discussing tax-risk management, the study may heighten audit committee members’ awareness of their role in this respect. Originality/value This study’s results indicate that even in a setting where incentives for firms to be tax-aggressive is low compared to high-tax rate countries, there is variability in firms’ tax aggressiveness. This situation allows us to find audit committee characteristics that are effective in decreasing tax aggressiveness.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".