Value Added Tax: An Instrument used in some African Countries to Meet Fiscal Objectives
Bibliographic record
Abstract
This article aims to evaluate the extent at which VAT is used as an instrument by countries to meet their fiscal deficit and meet the needs of their citizenry.Taxpayers pay taxes based on their ability to pay and with an anticipation that they will receive services in return to their contribution from government.The VAT due to its buoyancy nature contributes sizable amount of taxes which alleviate the financial burden of countries in meeting the financial obligations.Numerous kinds of literature demonstrate that whenever countries experience any budget shortfall they always look for fiscal remedies in either introduction of VAT or changing the rate of VAT.South Africa recently changed its long term rate of 14 % VAT to 15%.This article is conceptual in approach and uses the literature to argue that Value Added Tax (VAT) can be used as an effective instrument to meet fiscal objectives in some African Countries.Countries have the responsibility to ensure that their subject contributes to taxes which amongst others should in the form of VAT.As in the case of other taxes, the taxes are used to meet the fiscal obligation a country faces.The paper concludes that many countries that have introduced VAT have managed to meet their fiscal obligation due to high revenue contribution that have emanated from it, making the VAT the best tax methods to enable the country to meet their fiscal obligations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".