The case for minimum unit prices on alcohol in South Africa
Bibliographic record
Abstract
BACKGROUND: Although only about a third of South African (SA) adults indicate that they consume alcohol, heavy drinking is common. As a result, society carries large alcohol-related mortality and economic burdens. OBJECTIVES: To investigate the feasibility of a minimum unit price (MUP) on alcohol, aimed at reducing the prevalence of heavy drinking. METHODS: The study calculates unit values, defined as total monthly alcohol expenditure per household, divided by the household's total monthly alcohol consumption, for four categories of drinking households (moderate, intermediate, occasional heavy and regular heavy), using wave 4 data (2015) from the National Income Dynamics Study. A cumulative distribution of the unit values is derived for each of the four categories of drinking households. A number of hypothetical MUPs are imposed, and the impact of these MUPs on the consumption of the different categories of drinking households is estimated, taking cognisance of the fact that these households respond differently to price changes. Moderately drinking households tend to be more price sensitive than regular heavy-drinking households. RESULTS: Occasional and regular heavy-drinking households comprise a quarter of all households (and half of all drinking households) in SA, but consume 84% of all alcohol consumed in the country. There are large differences in the calculated average price of alcohol between different categories of drinking households, ranging from ZAR12.00 per standard drink among moderately drinking households to ZAR1.53 per standard drink among regular heavy-drinking households. An MUP of ZAR3.00 (alternatively ZAR10.00) per standard drink is estimated to reduce alcohol consumption by 11.9% (21.8%) among regular heavy-drinking households, by 3.1% (11.6%) among occasional heavy-drinking households, by 2.3% (15.9%) among intermediate-drinking households and by 0.3% (6.1%) among moderately drinking households. CONCLUSIONS: An MUP on alcohol is not a silver bullet, but could have a significant impact on reducing the consumption of alcohol among regular heavy-drinking households, and to a lesser extent among occasional heavy-drinking and intermediate-drinking households. The government should strongly consider implementing such a policy.
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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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".