A look at support for national smoking bans through lenses of fairness and self interest
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A look at support for national smoking bans through lenses of fairness and self interest. Mplus user group meeting Bristol, 2009. Nigel Guenole [email protected] & Dr Sasha Chernyshenko, Dr Stephen Stark, Kiri Milne. Acknowledgements.

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

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

Mplus user group meeting

Bristol, 2009.Nigel Guenole

[email protected]

& Dr Sasha Chernyshenko, Dr Stephen Stark, Kiri Milne


Acknowledgements

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


Environmental tobacco smoke ets is a devastating health threat

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.


Reasons for the controversy over national smoking bans

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


A theoretical model of support based on theories of fairness and self interest

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


Research design

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


Sem tests of theoretical model at each of 3 time points

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


Empirical cross sectional sem findings for time 1

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


Basic linear growth model and some possible extensions

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


Growth model for support

Growth model for support

=26.48, df=25, p=.38, CFI=.99, TLI=.99, RMSEA=.02, * = significant p<.05


Fit statistics for each growth process

Fit statistics for each growth process


Growth models change results

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


The challenge

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)


Results

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


Implications

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?


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