How does Debt Tax Shield Moderate Corporate Governance Mechanisms and Income Tax Compliance in Nigerian Listed Companies?
Bibliographic record
Abstract
This study investigated how debt tax shields (DTS) moderate the relationship between corporate governance mechanisms and income tax compliance in Nigerian listed firms. Amid persistent tax revenue shortfalls and evolving corporate governance reforms, understanding the interplay between governance structures and financial strategies has become crucial in emerging markets. Using a panel dataset of 92 non-financial firms listed on the Nigerian Exchange Group (NGX) from 2013 to 2022, the study adopts fixed-effects regression models to examine the direct and moderating effects of three key governance mechanisms: board gender diversity (BGD), audit committee size (BAC), and managerial ownership (MO) on income tax compliance, proxied by the effective tax rate (ETR). Findings reveal that BAC positively and significantly influences tax compliance in most model specifications, reinforcing the importance of board-level oversight. MO is significant in selected models, supporting the incentive alignment argument, though not consistently across all specifications. BGD does not exhibit a direct effect but demonstrates a significant positive interaction with DTS, suggesting that gender-diverse boards are more effective in leveraged firms where financial complexity heightens compliance risk. Among the control variables, profitability (ROA) and firm size (Fsize) consistently predict higher tax compliance, while leverage (LEV), DTS, and industry classification (IND) show no direct effects. The study concludes that governance mechanisms do not operate in isolation but are conditioned by firms' capital structures. Policymakers and regulators are advised to integrate governance reforms with financial risk profiling for enhanced compliance enforcement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".