More on the Positive Fiscal and Health Effects of Increasing Tobacco Taxes in Nigeria
Bibliographic record
Abstract
Nigeria is faced with substantial economic and health burdens caused by tobacco smoking. The economic burden of smoking accounts for approximately 1.3 per cent of Nigeria's GDP. In terms of its health impact, 4.9 per cent of all deaths in 2019 were attributed to smokingrelated diseases. The thousands of Nigerians that die annually from tobacco-induced diseases are no longer able to contribute productively to the economy. Tobacco taxation is one very effective mechanism for reducing the burden of smoking. This paper measures and benchmarks the economic gains and the number of lives that could be saved through increased tobacco taxation in Nigeria. Should the government of Nigeria increase the excise tax to 240 Naira per pack (together with an ad valorem tax of 50 per cent of the CIF/ex-works price), our model predicts that, over 30 years, nearly 150,000 premature deaths could be avoided. This is in addition to the more than 150 per cent increase in government revenue that would also result. The model indicates that the larger the increase in the excise tax, the greater would be its fiscal and public health impact.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".