Differential responsiveness to cigarette price by education and income among adult urban Chinese smokers: findings from the ITC China Survey
Bibliographic record
Abstract
BACKGROUND: Few studies have examined the impact of tobacco tax and price policies in China. In addition, very little is known about the differential responses to tax and price increases based on socioeconomic status in China. OBJECTIVE: To estimate the conditional cigarette consumption price elasticity among adult urban smokers in China and to examine the differential responses to cigarette price increases among groups with different income and/or educational levels. METHODS: Multivariate analyses employing the general estimating equations method were conducted using the first three waves of the International Tobacco Control (ITC) China Survey. Analyses based on subsample by education and income were conducted. FINDINGS: Conditional cigarette demand price elasticity ranges from -0.12 to -0.14. No differential responses to cigarette price increase were found across education levels. The price elasticity estimates do not differ between high-income smokers and medium-income smokers. Cigarette consumption among low-income smokers did not decrease after a price increase, at least among those who continued to smoke. CONCLUSIONS: Relative to other low-income and middle-income countries, cigarette consumption among Chinese adult smokers is not very sensitive to changes in cigarette prices. The total impact of cigarette price increase would be larger if its impact on smoking initiation and cessation, as well as the price-reducing behaviours such as brand switching and trading down, were taken into account.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".