Effect of E-Tax Payment on Revenue Generation in Nigeria
Bibliographic record
Abstract
The study examined the effect of e-tax payment on revenue generation in Nigeria. The study period covered six (6) years and three (3) quarters, spanning from the first quarter of 2012 to the second quarter of 2018. the period for pre e-taxation covered thirteen (13) quarters, spanning from the first quarter of 2012 to the first of 2015 while the period for post e-taxation covered thirteen (13) quarters, spanning from the second quarter of 2015 to the second quarter of 2018.The analysis was carried out using Trend analysis, descriptive statistics of mean and standard deviation, paired sampled t-test. The findings revealed that there was insignificant positive difference between pre and post value added tax revenue with t-statistics and p-value of 0.520 and 0.612 respectively. This connotes that e-tax payment has an insignificant positive effect on value added tax revenue in Nigeria. Similarly, it was discovered that there was a positive insignificant difference between pre and post company income tax revenue with t-statistics and p-value reported to be 0.833 and 0.421 respectively. That is, e-tax payment has negative insignificant impact on Value Added Tax (VAT) revenue. Lastly, the findings revealed that there is a positive insignificant difference between pre and post capital Gain tax revenue with t-statistics and p-value of 1.218 and 0.247 reported to be respectively. That is, that e-tax payment has a positive insignificant effect on company income tax revenue in Nigeria. It was therefore concluded that E-tax payment has not contributed to capital gain tax, value added tax and company income tax generation in Nigeria.
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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 | 0.004 | 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 | 0.000 | 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".