Long-Run Impacts of Increasing Tobacco Taxes: Evidence from South Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tobacco taxes are considered an effective policy tool to reduce tobacco consumption \nand produce long-run benefits that outweigh the costs associated \nwith a price increase. Through this policy, some of the most adverse effects and \neconomic costs of smoking can be reduced, including shorter life expectancy, \nhigher medical expenses, added years of disability among smokers, and the \neffects of secondhand smoke. Nonetheless, tobacco taxes are often considered \nregressive because low-income households tend to allocate a larger share of \ntheir budgets to purchasing tobacco products. This paper uses an extended \ncost-benefit analysis to estimate the distributional effect of tobacco taxes on \nhousehold welfare in South Africa. The analysis considers the effect on household \nincome through an increase in tobacco prices, changes in medical expenses, and \nthe prolongation of working years. Results indicate that a rise in tobacco prices \ninitially generates negative income variations across all groups in the population. \nIf benefits through lower medical expenses and an expansion in working years \nare considered, the negative effect is reduced, particularly in medium- and \nupper-bound elasticities. Consequently, the aggregate net effect is progressive \nand benefits the bottom deciles more than the richer ones. Overall, tobacco \ntax increases exert a small, but positive effect in the presence of low conditional \ntobacco price elasticity. If the population is more responsive to tobacco price \nchanges (or participation elasticity estimates are included) then they would \nexperience even more gains from the health and work benefits. More research \nis needed to clarify the distributional effects of tobacco taxation in South Africa.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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 it