Cigarette purchasing behaviour in Thailand and Malaysia: Comparative analysis of a semi-monopolistic and a free-market structure
Bibliographic record
Abstract
A wide range of cigarette prices can undermine the impact of tobacco tax policy when smokers switch to cheaper cigarettes instead of quitting. In order to better understand this behaviour, we study socio-economic determinants of price/brand choices in two different markets: a semi-monopolistic market in Thailand and a competitive market in Malaysia. The hypothesis that the factors affecting the price/brand choice are different in these two markets is analysed by employing a 2005 survey among smokers. This survey provides a unique perspective on market characteristics usually described only in business reports by the tobacco industry. We found that smokers in Thailand have fewer opportunities to trade down to save money on cigarettes, but pay lower prices than smokers in Malaysia, despite Thailand's higher tax rate. The Malaysian market, on the other hand, offers many possibilities to shop around for cheaper cigarettes. Higher income and education increase the price paid per cigarette in both countries, but the impact of these factors is larger in Malaysia. This has implications for sensitivity to cigarette prices. Using tax policy alone should be a more effective tobacco control measure in Thailand as compared to Malaysia. The effectiveness of a tax increase in Malaysia can be improved by adding programmes focusing on smoking cessation among low-income/low-educated smokers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".