The Impact of Income and Taxation in a Price-Tiered Cigarette Market: findings from the ITC Bangladesh Surveys
Bibliographic record
Abstract
BACKGROUND: Taxing tobacco is among the most effective measures of tobacco control. However, in a tiered market structure where multiple tiers of taxes coexist, the anticipated impact of tobacco taxes on consumption is complex. This paper investigates changing smoking behaviour in lieu of changing prices and changing income. The objective of the paper is to evaluate the effectiveness of change in prices (through taxes) and change in income in a price-tiered cigarette market. METHOD: A panel dataset from the International Tobacco Control Bangladesh surveys is used for analysis. For preliminary analysis transition matrices are developed. Next, probit and multinomial logit regression models are used to identify the effects of changes in prices and changes in income along with other control variables. FINDINGS: Transition matrices show significant movement of smokers across price tiers from one wave to another. Regression results show that higher income raises the probability to up-trade and decreases the probability to down-trade. Results also show that higher prices raises the probability to up-trade and reduces the probability to down-trade. Although not significant, there exists a negative relationship between the probability to down-trade and the probability to intend to quit. CONCLUSION: It is evident from the results that a price-tiered market provides smokers more opportunities to accommodate their smoking behaviour when faced with price and income change. Therefore, tiered structure of the tax system should be replaced with uniform taxes. Moreover, overall cigarette taxes need to be raised to an extent so that it off-sets any positive effects of income growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".