Effect of E-Taxation on Revenue Generation in Nigeria A Pre-Post Analysis
Bibliographic record
Abstract
The investigation analyzed the impact of e-tax assessment on income generation in Nigeria. The examination applied secondary data gotten from Federal Inland Revenue Service tax report and CBN Statistical release and Quarterly Economic Reports. These information were time arrangement information covers the period from first quarter of 2012 to second quarter of 2018 (that is, pre e-charge is from first quarter of 2012 to first quarter of 2015 while the post echarge is from second quarter of 2015 to second quarter of 2018). The information gathered were broke down utilizing Ordinary Least Square Method. The outcomes show an idealistic huge impact of pre (before the starter of e-tax assessment) company income tax and value added tax on income generation in Nigeria and a contrary immaterial impact of post organization annual duty income and value added assessment income on revenue generation in Nigeria (after the appearance of e-tax collection) at 5% level of critical. This implies E-tax collection has not contributed decidedly to both company income tax revenue and value added tax revenue generation in Nigeria; though there is an unwanted immaterial impact of pre and post capital gain charge income on income generation in Nigeria at 5% level of noteworthy. This implies Etax collection has not contributed decidedly to capital gain charge generation in Nigeria. The examination, along these lines among others prescribes that so as to amplify the foreseen positive impact of the activity, government through Federal Inland Revenue Services should work out modalities on the most proficient method to sharpen companies on the fundamentals of E-tax collection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".