The effect of cigarette prices on brand-switching in China: a longitudinal analysis of data from the ITC China Survey
Bibliographic record
Abstract
BACKGROUND: Recent studies have found that Chinese smokers are relatively unresponsive to cigarette prices. As the Chinese government contemplates higher tobacco taxes, it is important to understand the reasons for this low response. One possible explanation is that smokers buffer themselves from rising cigarette prices by switching to cheaper cigarette brands. OBJECTIVE: This study examines how cigarette prices influence consumers' choices of cigarette brands in China. METHODS: This study uses panel data from the first three waves of the International Tobacco Control China Survey, drawn from six large cities in China and collected between 2006 and 2009. The study sample includes 3477 smokers who are present in at least two waves (8552 person-years). Cigarette brands are sorted by price into four tiers, using excise tax categories to determine the cut-off for each tier. The analysis relies on a conditional logit model to identify the relationship between price and brand choice. FINDINGS: Overall, 38% of smokers switched price tiers from one wave to the next. A ¥1 change in the price of cigarettes alters the tier choice of 4-7% of smokers. Restricting the sample to those who chose each given tier at baseline, a ¥1 increase in price in a given tier would decrease the share choosing that tier by 4% for Tier 1 and 1-2% for Tiers 2 and 3. CONCLUSIONS: China's large price spread across cigarette brands appears to alter the brand selection of some consumers, especially smokers of cheaper brands. Tobacco pricing and tax policy can influence consumers' incentives to switch brands. In particular, whereas ad valorem taxes in a tiered pricing system like China's encourage trading down, specific excise taxes discourage the practice.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".