Assessing the âÂÂValueâ in Value Added Tax: Evidence from Nigerian Economy
Bibliographic record
Abstract
Value Added Tax (VAT) in Nigeria is a consumption tax that was established by the Value Added Tax Act of 1993. It is a Federal Tax which is managed by the Federal Inland Revenue Service (FIRS) of Nigeria. The essence of this paper is to re-evaluate the effectiveness and efficiency of the administration of VAT in Nigeria, as well as to appraise the benefits inherent in the adoption of VAT with respect to its impact on Nigerian economic growth within the period 1994-2014. To effectuate the objectives of the study, relevant secondary data were sourced from the Central Bank of Nigeria (CBN) Statistical Bulletin, Federal Inland Revenue Service (FIRS), and other relevant government agencies. The empirical analysis was based on multiple regression technique. Economic growth was proxy by Gross Domestic Product and the result shows that there is no significant relationship between Value Added Tax and Economic growth, there is a significant relationship between values added tax and the total revenue generated in Nigeria and that VAT administration in Nigeria is effective and not efficient. We recommended inter alia that the government should increase VAT rate for luxury goods such as tobacco, by so doing VAT will be made progressive with greater impact on the rich than the poor. More so the bracket of goods and services on which VAT is charged should be expanded, thus leading to an increase in VAT revenue.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".