Firm Attributes and Corporate Tax Aggressiveness: A Comparative Study of Nigeria and South Africa Banks
Bibliographic record
Abstract
The aim of this paper is to investigate the link between firm attributes and tax aggressiveness in Nigeria and South Africa. A comparative analysis was carried out on the variables of firm size, age, profitability, leverage, liquidity, complexity, foreign ownership and tax aggressiveness on banks in Nigeria and South Africa. The study employed the longitudinal research design and took a comparative analysis approach. The population consists of the 13 listed commercial banks quoted on the Nigerian Stock Exchange and the 16 local commercial banks listed on the Johannesburg Stock Exchange. The time frame for the study was from 2012-2020. Data collated was analysed using the techniques of descriptive statistic, correlation and panel data regression technique while MAPE and Theil’s inequality coefficient were used in evaluating the forecast abilities of the models. Two alternative measures of tax aggressiveness (GAAP-ETR and D_BTD) were adopted as dependent variables. The panel data collected was analysed. The result of the Nigerian model (using the D_BTD measure) showed that firm size and firm complexity both have a significant positive relationship with tax aggressiveness while firm age and profitability asserted significant negative impacts on tax aggressiveness. The outcome of the South Africa model (using the GAAP-ETR measure) showed that firm age and profitability have a significant negative relationship with tax aggressiveness while firm size and liquidity have significant positive relationships with tax aggressiveness. The study recommends, that regulatory bodies and tax authorities should beam their searchlight on tax saving strategies of small size companies with a view to effectively monitoring their aggressive tax avoidance schemes.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".