Taxation and Income Inequality in Nigeria
Bibliographic record
Abstract
The study examined taxation and income inequality (GINI), specifically, it determined the impact of Value Added Tax (VAT), Custom and Excise Duties (CED), Petroleum Profit Tax (PPT) and Company Income Tax (CIT) on GINI in Nigeria from the year 1990 to 2016. The Cointegration and Error Correction Models (ECMs) were used to analyze the data. Augmented Dickey Fuller unit root was used to test for stationarity. Data were sourced from the Central Bank of Nigerian statistical bulletin, Federal Inland Revenue Service and the National Bureau of Statistics. The result revealed that VAT, CED and PPT had positive relationship with GINI when measured at 5% critical level, though VAT and CED were not significant. CIT had a negative but significant impact on GINI. Based on the findings, we conclude that only CIT was able to reduce income inequality. We therefore recommend that VAT should be imposed on goods and services consumed by high income earners. In respect of CED, government should address the level of tariffs; for PPT, there is need for adequate diversification of the economy; and for CIT, tax authority should harness corporate taxes to its fullness.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".