Do Overlapped Audit Committee Directors Affect Tax Avoidance?
Bibliographic record
Abstract
This research is motivated by the Omani government’s desire to reduce tax avoidance and bolster tax revenue collected from financial institutions. The purpose of this paper is to examine the impact of overlapped audit committee (AC) chairs and other directors on tax avoidance practice and whether they play a monitoring or advisory role in tax avoidance practice. As a measure of overlapped AC chairs, we used a dummy variable to indicate whether an AC chair sits on other committees within a company or not. We used the proportion of AC members who serve on the AC and other committees within a company as our proxy for overlapped AC directors. We used a company’s cash effective tax rate as a proxy for tax avoidance. We regressed tax avoidance on overlapped AC membership and other control variables, using a sample of 204 firm-year observations from financial institutions listed on the Muscat Stock Exchange between 2014 and 2019. Our regression results show that a higher proportion of overlapped AC members and the presence of an overlapped AC chair were both associated with lower effective tax rates, which equated to more tax avoidance. This suggests that these directors play an advisory role in the Omani context. We found, however, that these directors play a monitoring role when firms take a loss. From these findings, we draw important implications for regulators who need to rethink the potential consequences of having overlapped AC chairs and AC directors. Our study focuses on Omani financial institutions, which are highly regulated and monitored by the central bank, and our findings may not be directly applicable to non-financial institutions that are less regulated, so caution is needed when interpreting the findings. Further research could employ a repeated measured research design, such as ours, and explore the same research question in non-financial institutions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".