Tax avoidance in banking institutions: an analysis of the top seven Nigerian banks
Bibliographic record
Abstract
Purpose The purpose of this paper is to review the quantum and magnitude of tax avoidance in Nigeria's top seven banks by using recognized tax avoidance proxies of the Generally Accepted Accounting Principles (GAAP) and the International Financial Reporting Standard (IFRS) effective tax rate (ETR) and book-tax gap analysis for the appraisal. Design/methodology/approach Data for the paper came from the annual reports of the banks between 2011 and 2019. The individual bank’s tax data was analyzed for trends and then consolidated to establish the average percentages and the exact amount of the tax the banks evaded each year and cumulatively over the review period. The data were then matched with analytics of the drivers of tax avoidance in the reconciliation statement to highlight essential tax planning items and strategies being exploited by each bank in the pursuit of aggressive tax avoidance behavior. Findings F-test comparing the aggregate means (all banks) for tax evasion proxies of ETR and the book-tax gap was conducted at a 95% confidence interval. The results of this paper indicate no significant difference between the means obtained, thus affirming that the same pattern of tax evasion was consistent among the banks for the years reviewed. Originality/value The findings of this paper highlight the tax avoidance behavior of the referenced banks, identify weaknesses in the corporate tax planning policy pursued and serve to alert policymakers of the need to strengthen the laws and block loopholes that provide rooms for unrestrained tax avoidance behavior in the banking sector.
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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 | 0.001 | 0.002 |
| 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".