Effects of comprehensive smoke-free legislation on smoking behaviours and macroeconomic outcomes in Shanghai, China: a difference-in-differences analysis and modelling study
Bibliographic record
Abstract
BACKGROUND: China has one of the highest levels of tobacco consumption globally, and there is no national smoke-free legislation. Although more than 20 Chinese cities have passed local smoke-free laws since 2008, evidence on their effectiveness in reducing smoking behaviours and their economic benefits is scarce. By exploiting a natural quasi-experiment, whereby a comprehensive public smoking ban was implemented in Shanghai in March, 2017, this study aims to assess the impact of the policy on individual smoking behaviours and quantify its effect on macroeconomic outcomes. METHODS: In this difference-in-differences analysis and modelling study, we used data on smoking behaviours from the 2012, 2014, 2016, and 2018 waves of the China Family Panel Studies. We used a difference-in-differences approach to investigate trends in smoking prevalence in respondents in Shanghai, relative to respondents from other direct-administered municipalities, provincial capital cities, and subprovincial municipalities (control group), after the implementation of a smoking ban in 2017. All respondents aged 18 years or older were included, with the exception of people who lived in Beijing and rural areas. The primary variable of interest in the difference-in-differences analysis was self-reported smoking status. Based on the difference-in-differences estimation of reduction in smoking prevalence, we then used a health-augmented macroeconomic model to estimate the potential macroeconomic gains if such a ban was implemented across China for the period 2017-35. FINDINGS: 14 688 respondents were included in the analysis: 5766 from Shanghai and 8922 from the control group. After the implementation of the smoking ban in Shanghai in 2017, smoking prevalence decreased by 2·2 percentage points (95% CI 2·1-2·3), equivalent to an 8·4% reduction in the number of current smokers. The smoking ban had a larger effect on men, people with a higher level of education, unmarried people, and younger people when compared with their respective counterparts. The modelling analysis showed that implementing a nationwide comprehensive public smoking ban similar to that in Shanghai would result in a 0·04-0·07% increase in the national gross domestic product in China between 2017 and 2035, outweighing the economic costs of smoking ban enforcement. INTERPRETATION: The smoking ban in Shanghai shows that a comprehensive public smoking ban with strict enforcement is effective in curbing smoking behaviours. Moreover, the implementation of a comprehensive public smoking ban across China would be cost-effective. FUNDING: National Social Science Fund of China.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".