The effects of price and non-price policies on cigarette consumption in South Africa
Bibliographic record
Abstract
INTRODUCTION: The health consequences of smoking are serious and have been frequently detailed. A reduction in tobacco-related mortality hinges upon the ability to reduce tobacco usage. There is overwhelming evidence that higher cigarette prices reduce the demand for cigarettes, but little is known about the combined effect of price and non-price policies. This paper seeks to extend the analysis of price elasticities by estimating the combined effect of changes in price and non-price legislations in South Africa. METHODS: Annual time-series data from 1961 to 2016 are used, with a policy index constructed to capture the instances of non-price tobacco legislation. We estimate the combined impact of changes in tobacco control policy on cigarette consumption using a vector error correction model (VECM) and a two-stage least squares (2SLS) model. RESULTS: The estimated long-run own-price elasticities lie between -0.55 and -0.72, while the income elasticities lie between 0.39 and 0.49. The coefficients of the changing tobacco control policies and the changing market structure show that they contribute to a modest reduction in cigarette consumption. The short-run deviations from the steady state are presented using the error correction term (ECT). CONCLUSIONS: Cigarette demand is responsive to cigarette prices and non-pricing policies but failure to control for non-pricing policies overstates the price effect. This suggests that both cigarette prices and non-pricing legislation are effective in reducing cigarette consumption.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".