Tax Aggressiveness, Corporate Governance and Audit Fees: A Study of Listed Firms in Nigeria
Bibliographic record
Abstract
The concept of audit fee has received immense empirical investigation in literature. However, these vast studies have not sufficiently explored the relation of the concept with tax aggressiveness and corporate governance. This study therefore sought to provide empirical evidence as to whether tax aggressive and corporate governance mechanisms are significantly associated with audit fees among listed firms in Nigeria. Leaning on the agency and stakeholder theories, the study examined the measures of tax aggressiveness of effective tax rate and cash tax rate as well as corporate governance mechanisms of board gender diversity, audit committee diligence, and board independence; and how these variables explain changes in external audit fees. A sample of one hundred and seven (107) firms from the entire firms quoted on the Nigerian Stock Exchange as at December, 2018 was utilised. Data were sourced solely from annual financial statements of the studied firms over a ten-year period (2009 to 2018). The panel regression technique, with preference for the random effect model based on the outcome of the Hausman test, was employed to estimate the balanced panel data. The results of the study showed that cash tax rate, audit committee diligence and board independence all exert positive and significant effect on audit fees. Surprisingly, the study revealed a positive but statistically insignificant link between board gender diversity and audit fees. This result may not be unconnected with the low presence of female directors on the board of the firms investigated. In light of the findings, we therefore recommend that more female gender should be allowed to sit on the boards of listed firms in Nigeria in line with the Norwegian model of 40% female gender representation and the Federal Government 35% Affirmative Action. We also recommend that board independence should be encouraged more so as to enhance their oversight functions, and promote quality financial reporting and audit amongst listed firms in Nigeria.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".