A look at support for national smoking bans through lenses of fairness and self interest

1. A look at support for national smoking bans through lenses of fairness and self interest Mplus user group meeting Bristol, 2009.Nigel Guenole n.guenole@gold.ac.uk & Dr Sasha Chernyshenko, Dr Stephen Stark, Kiri Milne

2. Acknowledgements • This research was sponsored by the New Zealand Ministry of Health and overseen by the New Zealand Health Sponsorship Council • Anaru Waa and Sue Walker for thoughtful comments on this research

3. Environmental tobacco smoke (ETS) is a devastating health threat • Environmental tobacco smoke is a serious health threat e.g. 2001 to 2004 in US alone, estimated 443,000 deaths attributable to smoking, 5.1 million years of potential life were lost, annual lost productivity was $96.8 billion (Centres for Disease Control) • Bans occurring locally and nationally, but not all countries yet • By understanding causes of support we could aid introduction of bans & help prevent reversals e.g. Geneva (Reuters, 2008) • We undertook this study amongst a cohort of bar managers in New Zealand where a ban was introduced in November 2004.

4. Reasons for the controversy over national smoking bans • Opponents of national smoking bans typically raise three objections • The health threats of ETS exaggerated • The industry will suffer economically • Bans infringe smokers’ rights • Origins in either fairness or self-interest, from these theories we can try to formulate a model of the causes and consequences of support • Distributive fairness concerns more relevant here as fair process effect not as powerful when outcomes threaten identity, as is likely with smoking related outcomes

5. Personal Implications - Support for Smoking Ban - Economic Implications + + Workers’ Rights Patrons’ Rights + + Environmental Tobacco Smoke Beliefs A theoretical model of support based on theories of fairness and self-interest Direct effect for ETS = reductionist perspective on justice, unnecessary, but we’ll test it anyhow

6. Research design • 3-wave cohort telephone survey, November 2004 (prior), April 2005, October 2005 • Sampling frame was a register of all alcohol-licensed venues (approximately 3000) in New Zealand in October 2004. • Randomly selected a sample list of 900 establishments, of which 705 were still in business. • 535 at time 1, 346 at time 2, 255 at time 3 • 4 questions per construct in theoretical model, demographic questions, enforcement behaviour at times 2 and 3 only

7. SEM tests of theoretical model at each of 3 time-points • Good fit for measurement & structural models at all three time points • Measurement & structural equivalence for smoker non-smoker and work-owner subgroups • Expected signs for all structural paths, all paths significant • Main effects for smoking status and owner worker populations but no moderation • Very similar structural parameter estimates across time for all paths • Model constraints indicated justice component of the model the strongest predictor at all time points

8. Personal Implications -.10 Support for Smoking Ban -.13 Economic Implications .49 .34 Workers’ Rights Patrons’ Rights .89 .86 Environmental Tobacco Smoke Beliefs Empirical cross-sectional SEM findings for Time 1 =825.76, df=243, p=<.01, CFI=.92, TLI=.91, RMSEA=.07, SRMR=.06, all parameters significant p<.05

9. µ = 0 Basic linear growth model and some possible extensions µ σ2 σ2 Support Slope Support Intercept S1 S2 S3 • This growth model is just a very restrictive factor analysis model • The model can be extended in some very useful ways

10. Growth model for support =26.48, df=25, p=.38, CFI=.99, TLI=.99, RMSEA=.02, * = significant p<.05

11. Fit statistics for each growth process

12. Growth models change results A no growth model for ETS beliefs is the best fit for this construct Non-linearity for Patrons’ rights (slowing) by chi-square difference test

13. The challenge • Estimate a parsimonious multivariate growth model of the nomology of support, taking into account everything we have found up to this point, i.e. it is fairness that is most important, including the demographic covariates that must be also modelled, and adding a critical distal outcome variable (bar manager enforcement behaviour)

14. Results Smoke1 Smoke2 Smoke3 -.46 -.46 -.46 S1b S2b S1 S2 S1a S2a S3b S3 S3a Support Time 3 .42 Support Time 2 .15 Support Time 1 -.13 .28 Support Intercept ENFORCE .28 Support Growth 1.05 .24 Environmental Tobacco smoke Beliefs at Time 1 .24 .24 Health2 Health3 Health1 =26.48, df=25, p=.38, CFI=.99, TLI=.99, RMSEA=.02, all parameters significant p<.05. ETS1 ETS2 ETS3 • Fairness matters most, it is driven by stable ETS beliefs • Smoking and health vary over time & have direct effects • Growth factors predict enforcement behaviour

15. Implications • Support for bans is primarily determined by concerns with fairness, self-interest concerns narrowly defined (i.e., economic) are somewhat of a red herring, • Support has important consequences, i.e., whether bans will be enforced, as growth factors significantly related to enforcement behaviour • Support increases consistently over time • Persuading bar managers of the dangers of ETS, or the more malleable mediator, i.e., the fairness of smoking bans, could lead to more support. • Explore mixture modeling to identify dissenting classes, such as probably existed in Geneva where a smoking ban was recently reversed?