Moving Upstream: The Effect of Tobacco Clean Air Restrictions on Educational Inequalities in Smoking Among Young Adults

Abstract

Education affords a range of direct and indirect benefits that promote longer and healthier lives and stratify health lifestyles. We use tobacco clean air policies to examine whether policies that apply universally—interventions that bypass individuals’ unequal access and ability to employ flexible resources to avoid health hazards—have an effect on educational inequalities in health behaviors. We test theoretically informed but competing hypotheses that these policies either amplify or attenuate the association between education and smoking behavior. Our results provide evidence that interventions that move upstream to apply universally regardless of individual educational attainment—here, tobacco clean air policies—are particularly effective among young adults with the lowest levels of parental or individual educational attainment. These findings provide important evidence that upstream approaches may disrupt persistent educational inequalities in health behaviors. In doing so, they provide opportunities to intervene on behaviors in early adulthood that contribute to disparities in morbidity and mortality later in the life course. These findings also help assuage concerns that tobacco clean air policies increase educational inequalities in smoking by stigmatizing those with the fewest resources.

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Notes

  1. 1.

    We distinguish between tobacco clean air acts (i.e., smoking bans) and other tobacco-related policies that are less universal in their application, including (1) excise taxes, (2) ad restrictions, and (3) single cigarette sales restrictions. Such other policies are more fundamentally dependent on individual use of the flexible resources conferred by education. In this manner, they are less comprehensive and universal in their application and effect.

  2. 2.

    Before 2004, only metropolitan statistical area (MSA) was available. Although it increases time points, using MSA (>50,000 people) rather than CBSA (>10,000 people) reduces the number of cities (and respondents) analyzed. Given our focus on local policy, we prioritize adding more cities over time points while diversifying the cities given the lower population threshold of CBSAs, such that not only large cities are included. Although we restrict analyses to age 19 and older, we do not view this restriction as negative because this constitutes an age when young adults begin to frequent nightlife establishments, which have the most between-city variation in smoking bans.

  3. 3.

    The rate of any smoking in the past 30 days in the sample (34.5 %) compares well with that of young adults (18–25) in the National Survey on Drug Use and Health across these years, decreasing from 39.5 % in 2004 to 33.5 % by 2011 (Substance Abuse and Mental Health Services Administration (SAMHSA) 2016:7).

  4. 4.

    Because the parental measure is derived from years of schooling completed, we cannot be certain where those with a GED would group themselves (i.e., “12 years” or a lower year based on when they left formal schooling).

  5. 5.

    Although associate’s degree recipients are a small group, we separate them because research shows that relative to leaving college without a degree, the returns to earning an associate’s degree are greater and help its recipients weather recessionary periods (Kalogrides and Grodsky 2011; Kane and Rouse 1995; Vuolo et al. 2016). In many cases, an associate’s degree is a credential for vocational preparation, leading to tracking into occupations that require specialized knowledge in a way that dropping out of college would not. Thus, the associate’s degree group likely has more flexible resources to tap into than those who do not complete college.

  6. 6.

    The main drawback for LPMs is that predicted probabilities are not bounded between 0 and 1, but this is not an issue for our predictions.

  7. 7.

    Although the models shown can produce in-sample predicted probabilities, they cannot produce out-of-sample predicted probabilities because multiplying the smoking ban effect by 0 in the interaction model also eliminates the education variables from the model (see StataCorp 2017:447–450). We can, however, derive these predicted probabilities from separate models by education subgroup, which is the approach we take here. These separate models are shown in Tables A2 and A3 in the online appendix. As expected, the smoking ban coefficient is similar to that of the main effect in the interaction models for those with less than a high school education, and to the sum of the main effect and the respective education category for the higher levels of education. Any slight differences between the two modeling approaches are due to differential effect of the controls across education groups. For both parental attainment and individual attainment, the significant effects for those with less than a high school education are reaffirmed; the small, nonsignificant effects computed in the interaction models are also reaffirmed, now corresponding to the coefficients of the respective education categories. Although we caution against direct comparison of the coefficients, the two modeling approaches produce very similar results in terms of the magnitudes of the effects, providing additional evidence for the robustness of our results.

  8. 8.

    The calculated smoking ban effect for those with an associate’s degree is –0.068 (p < .01).

  9. 9.

    For comparison, the coefficient for high school diploma or GED (Table 3, Model 6) is 0.068; when GED is removed (Table A4, online appendix), this coefficient is similar at 0.054. In both cases, the difference in the effect of smoking bans from those with less than a high school education is significant.

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Acknowledgments

This work was supported by the National Institute on Drug Abuse (Grant #R03DA034933; PI: Vuolo). This research was conducted with restricted access to Bureau of Labor Statistics (BLS) data. The authors would like to thank the staff at the American Nonsmokers’ Rights Foundation (ANRF), particularly Maggie Hopkins and Laura Walpert. The views expressed here do not necessarily reflect the views of the BLS, NIDA, or ANRF. We also thank Joy Kadowaki, Emily Harris, Alexandra Marin, Jake Brosius, and Emily Ekl for research assistance, and Andrew Halpern-Manners for feedback on early drafts. Versions of this research have been presented at the 2018 annual meetings of the Population Association of America and the Society for Longitudinal and Life Course Studies.

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Hernandez, E.M., Vuolo, M., Frizzell, L.C. et al. Moving Upstream: The Effect of Tobacco Clean Air Restrictions on Educational Inequalities in Smoking Among Young Adults. Demography 56, 1693–1721 (2019). https://doi.org/10.1007/s13524-019-00805-2

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Keywords

  • Education
  • Smoking bans
  • Tobacco Use
  • Health inequality
  • Policy intervention