Smoking cessation in South Africa: cigarette prices, plain packaging, and illicit trade
Notice bibliographique
Résumé
The dangers of smoking are well-known and no longer contested. Despite this, many smokers struggle to quit smoking. The aim of this thesis is to investigate quitting behaviour in South Africa. In the second chapter, I investigate, using survival analysis techniques, whether cigarette prices affect smokers' decision to quit smoking. The analysis was done using nationally representative data, the National Income Dynamics Study (NIDS). Self-reported information for onset age and cessation age was used to create smoking histories from 1970 to 2017. Each smoker was followed from the time they started smoking, either until they quit, or until the last interview if they did not quit. Monthly price data, sourced from government documents, was merged with NIDS data. Various model specifications were estimated to test the robustness of the results. In the third chapter, I investigate whether the type of cigarette packaging, a matter which is currently being considered in South Africa, reduces the utility of cigarettes. Preferences were elicited using a discrete choice experiment. Data were collected in 2021 from University of Cape Town students. Both smokers and non-smokers were sampled. Intention to buy, intention to try, and perceptions of harm were investigated using conditional logit models. The attributes included packaging, price, and warnings on individual cigarettes. The design of the experiment accounted for illicit cigarettes so as to reflect current market conditions closely. The willingness to pay for cigarette packs with different attributes was also estimated using a Becker–DeGroot– Marschak auction. Since increasing excise taxes increases the demand for low-priced, untaxed cigarettes, smokers may switch to low-priced cigarettes instead of quitting. In the fourth chapter, I investigate the illicit cigarette market using gap analysis. Gap analysis is based on a comparison of consumption estimates (from survey data) with legitimate sales (as declared to the excise tax authority). The gap between self-reported consumption (scaled up to account for underreporting) and legitimate sales is an indication of the size of the illicit market. Self-reported consumption was estimated using two nationally representative surveys, NIDS and the All Media and Products Survey, allowing a long period (2002 to 2017) to be investigated. I also investigate the relationship between excise tax increases and illicit trade. The results from the second chapter indicate that price is a significant determinant of smoking cessation. A 10% increase in the price of cigarettes was estimated to result in a 5.5%−8.6% increase in smoking cessation. Females are more likely to quit than males. Respondents with higher education are more likely to quit compared to those with less education. Results from the third chapter indicate that plain packaging would be effective in reducing people's utility for cigarettes. I found that smokers reported preferring not to buy plain packs and non-smokers preferred not to try plain packs. In terms of health risk, both smokers and non-smokers perceived plain packs to be the most risky to health. My estimates from chapter 4 show that, between 2002 and 2009, the illicit cigarette market accounted for around 5% of the total market. Since 2009, the illicit cigarette market has increased sharply: by 2017 illicit trade accounted for 30%−35% of the total market. I found no evidence that excise tax increases were linked to an increase in illicit trade. When excise taxes were increasing rapidly, illicit trade was stable (2002−2009). On the other hand, when excise tax increases were relatively modest, illicit trade increased rapidly (2009−2017). The results have several policy implications. South Africa should continue to increase the price of cigarettes through excise tax increases to encourage smoking cessation. If consumers are able to buy cheaper illicit cigarettes, the impact of price increases is likely to be reduced. The South African government should therefore implement measures to reduce the illicit trade in cigarettes, as outlined by the WHO's Protocol to Eliminate Illicit Trade in Tobacco Products. The 2018 South African Control of Tobacco Products and Electronic Delivery Systems Bill, which, amongst other things, obliges tobacco manufactures to remove all branding and to include a graphic health warning, should be implemented.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».