Estimating the price elasticity of demand for cigarettes in South Africa using the Deaton approach
Bibliographic record
Abstract
OBJECTIVE: To estimate the price elasticity of demand for South Africa and thereby contribute to growing the evidence base of the likely impact of excise taxes on cigarette demand in low-income and middle-income countries. METHODS: We employ the Deaton method, using wave 5 data from the South African National Income Dynamics Study, to estimate the cigarette price elasticity for South Africa. We used a sample of 6820 households. RESULTS: Of the 6 820 households in the sample for which we had sufficient data, 1341 (19.7%) spent money on tobacco. The price elasticity of demand for cigarettes is estimated at -0.86 (95% CI -1.37 to -0.35), implying that the demand for cigarettes in South Africa declines by 8.6% for every 10% increase in price. CONCLUSION: The negative price elasticity estimate for South Africa indicates that increases in the excise tax are particularly effective in controlling cigarette consumption. However, given the presence of a significant illicit tobacco market in the country, it is important that authorities augment tax measures with strategies that curb the illicit trade in cigarettes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".