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School personnel smoking, school-level policies, and adolescent smoking in low- and middle-income countries
  1. Silda Nikaj1,
  2. Frank Chaloupka2
  1. 1Department of Economics, Texas Christian University, Fort Worth, Texas, USA
  2. 2Department of Economics, Institute for Health Research and Policy, University of Illinois at Chicago, Chicago, Illinois, USA
  1. Correspondence to Dr Silda Nikaj, Department of Economics, Texas Christian University, TCU Box 298510, 2800 S. University Drive, Fort Worth, TX 76129, USA; s.nikaj{at}tcu.edu

Abstract

Objectives This paper examines the link between personnel and teacher smoking on school grounds, and student smoking in 62 low-income and middle-income countries.

Methods We use a two-part model to estimate the effect of smoking by school personnel on youth smoking. In the first part, we model the decision to smoke for all students, using a linear probability model. In the second part, we estimate cigarette consumption among smokers. We employ country fixed effects to address country-level time-invariant unobservable factors and control for an array of local-level variables to address local-level heterogeneity.

Results We find that smoking by personnel and teachers on school grounds is associated with higher smoking prevalence among all youths, and higher cigarette consumption among female smokers. Our findings suggest that consumption among female smokers is primarily affected by smoking among female personnel, and that younger personnel/teachers appear to be more influential in determining behaviours among young people. In addition, we find that smoking restrictions on staff are associated with reductions in average consumption among female students.

Conclusions Low-income and middle-income countries may reduce smoking among young people by banning smoking for teachers and school personnel on school grounds.

  • Secondhand smoke
  • Environment
  • Prevention
  • Priority/special populations
  • Public policy
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Introduction

Because of the burden of death and disease caused by smoking, developed countries have invested in comprehensive tobacco control programmes that have curbed smoking among population groups.1 ,2 Smoking presents a growing public health problem in low-income and middle-income countries where lack of smoking restrictions, low levels of cigarette taxation, and high affordability of cigarettes have contributed to increasing levels of cigarette use.1–6 Recent research on low-income and middle-income countries finds that young people are more responsive to cigarette price changes than youth in developed countries, highlighting the importance of taxation as a policy tool in reducing smoking among the populations of developing countries.7–15

In recent years, the impact of social factors on health behaviours has received increasing attention from researchers. This research arises from the understanding that people make choices within a social context, and that the social context may shift the costs and benefits of particular actions.16–18 An extensive amount of literature in developed countries finds that peer and parental smoking have large impacts on adolescent smoking.19–25 Similarly, evidence suggests that school environments and restrictions on smoking in school may reduce smoking prevalence and consumption among youth.26–33 Despite the extensive evidence linking social environments and youth smoking in high-income countries, little evidence exists on the impact of school environments and smoking among youth in low-income and middle-income countries.31 This paper is the first to investigate associations among school personnel smoking, school-level policies and adolescent smoking behaviours, in 62 low-income and middle-income countries.

School environments and school personnel can affect behaviours among young people along several pathways. First, behaviour of school personnel likely serves as a model for appropriate behaviour for young people. If smoking among school personnel is common, then young people are likely to interpret such signals as appropriate or ‘adult’ behaviour.18 Second, it becomes increasingly difficult for personnel who smoke to discuss the dangers of smoking with students. Third, personnel who smoke on school grounds may be less likely to enforce smoking restrictions among students. Finally, personnel who smoke may stimulate consumption among student smokers not only by exposing them to secondhand smoke but also providing the visual stimuli or cues for smoking on school grounds.34

Researchers face several challenges in estimating the impact of personnel smoking on youth smoking. Unobserved factors and school selection/sorting confound the effect of social influence on individual behaviours.35 Personnel and students may select schools based on smoking behaviours or other unobserved characteristics correlated with smoking. Simple associations between personnel and youth smoking would capture both the effect of personnel smoking behaviours and sorting. Without appropriate controls, sorting would overstate the true impact of school personnel's behaviours on youth smoking.

Second, bi-directionality between individual behaviour and the behaviours of those in one's social circle presents further methodological challenges. In the peer context, it is unclear whether peer smoking is affecting individual smoking as there is invariably some effect exerted from individual smoking on peer smoking. The bi-directionality is especially pronounced among individuals who occupy the same social standing in a network, but researchers see it as less of a concern among individuals of different social standings.36 This line of reasoning posits that school personnel occupy a position of authority and exhibit some social distance from students, and thus are unlikely to be affected by student smoking behaviour directly.

Finally, high rates of smoking among students and personnel may be the result of an environment that lacks tobacco control restrictions. Naïve analyses would produce high rates of correlation in smoking behaviours among population groups; however, smoking among all groups would be influenced by shared contextual factors that affect everyone in a social network. Without controls for sorting of students and personnel into schools, the bi-directionality of influences in a group, and the local-level environment, estimates of the behavioural response to smoking in one's social network will be biased.

This paper estimates the associations between smoking by school personnel, school-level restrictions, and smoking by middle and high school students for 62 countries that participated in the Global Youth Tobacco Survey (GYTS) and Global School Personnel Survey (GSPS). We hypothesise that students exposed to teachers and personnel who smoke on school grounds, will be more likely to smoke and have higher levels of average cigarette consumption. Furthermore, we test the hypothesis that stronger smoking restrictions on school grounds are associated with lower levels of smoking prevalence and consumption among young people. We control for an array of local-level, tobacco-related characteristics that reduce the likelihood of bias in estimates,i and capture country-specific characteristics that are unobservable, through the use of country fixed effects.ii

Data and methods

The data used are pooled cross-sections from 62 countries of GYTS and GSPS from 2002 to 2008. The GYTS is a school-based survey that examines cigarette use, knowledge and attitudes among youths 11–19 years of age. The survey uses a sample design that chooses schools at random within a given country and then randomly surveys classes within these schools. Within the same schools, a second survey (GSPS) examined cigarette use, knowledge and attitudes about smoking among school personnel. The survey was voluntary and not all schools participating in the GYTS participated in the GSPS, thus limiting the sample of countries that participated in both surveys to a total of 77. Many of the survey questions vary over time, which does not allow exact matching of control variables across different waves of the survey for several countries. The survey question mismatch and exclusion of high-income countries limits the number of countries to a total of 62 for the current analysis. A detailed list of excluded countries as well as those in the analysis is provided in online supplementary appendix A.

We employ two measures of smoking in our analysis. Smoking participation is a dichotomous variable that takes a value of 1 if the student smoked at least one cigarette in the month before the survey, and 0 otherwise. We construct smoking intensity (consumption) by multiplying the number of days that smoking occurred in the past month by the average number of cigarettes smoked daily. Table 1 provides summary statistics of the analysis variables.

Table 1

Definition of analysis variables and summary statistics

The all-sample smoking prevalence is 11%. The average smoker consumes 53 cigarettes a month. Individual-level explanatory variables include age, gender, measures of parental smoking and whether the youth had instruction in school about the dangers of smoking in the last year.

The variable of interest for this analysis is the share of school personnel (personnel smoking) who smoke on school grounds. We constructed the variable by aggregating school personnel responses from the GSPS to a question of whether they had ever smoked cigarettes on school property during the last year. On average, 13% of all personnel had smoked on school property in the past year, while close to 18% of all school personnel are smokers. Over 70% of smokers among personnel continue smoking on school premises, suggesting that smoking is commonplace in schools.

We include several variables that control for the local tobacco-related environment: exposure to antismoking media, exposure to cigarette advertising and access to commercial cigarettes. We constructed all variables by aggregating student responses at the school level in order to reduce potential endogeneityiii of individual responses. Exposure to antismoking media is the percentage of non-smoking students who report recent exposure to antismoking media messages in broadcast and print media. We define exposure to cigarette advertising as the percentage of non-smoking students who report recent exposure to advertising in newspapers and billboards.iii Reduced access to commercial cigarettes is the percentage of students who report having been denied cigarette sales by local vendors due to their age.

Finally, we include controls for smoking restrictions on school grounds. More particularly, we included three variables by aggregating responses from the GSPS survey at the school level. The survey asked personnel whether the school had a ban on smoking on school grounds for students (student ban), a ban for personnel (staff ban), and whether the school enforced the ban for personnel as well as for students (enforcement). These variables do not capture the impact of smoke-free policies, as from the data it is impossible to discern which schools have instituted smoke-free policies, but they may provide an estimate of the plausible impact of such policies.iv

To control for country-specific unobservable characteristics that could drive smoking, we include country fixed effects or dummy variables. We capture changes in smoking over time by including linear and quadratic time trends. Because GYTS and GSPS survey most countries only once, year dummies would be almost perfectly collinear with country dummies and cannot be utilised. To address missing responses, we impute missing data for control variables.39

Empirical specification

We use a two-part model to estimate the effect of smoking by school personnel and school-level variables on youth smoking. In the first part, we model smoking participation for all students using a linear probability model. In the second part, we estimate cigarette consumption among smokers. The conditional demand estimation uses a generalised linear model (GLM) with a Gamma distribution and log-link.40 ,41 We cluster all standard errors at the school level.

Results

OLS results for smoking participation

Table 2 summarises results for smoking prevalence. We only present the fully specified model. We ran several models (not shown) by including/excluding different controls, to make sure that our estimates were robust and did not change. The point estimate—0.063—of personnel smoking at school is highly significant. A 10-percentage point increase in smoking on school grounds by personnel is associated with an increase in smoking prevalence of 0.63 percentage points—a 5.7% increase in smoking prevalence. Smoking by school personnel is linked to a larger absolute effect on smoking prevalence among boys than among girls (parameter estimates of 0.069 vs 0.05). However, the relative effects on smoking prevalence for a 10-percentage point increase in smoking among personnel are 4.6% and 7.1% for boys and girls, respectively.

Table 2

Linear probability model of smoking participation

Other controls have the expected signs. Males are more likely to smoke than females. Maternal smoking and paternal smoking are associated with increases in the probability of smoking by 11.1 and 4.6 percentage points, respectively, highlighting the large influence parents can exert on their children's smoking. Greater exposure to cigarette advertising is associated with increased smoking prevalence. Exposure to antismoking media is linked with a decrease in prevalence, but the association is significant only at the 10% level. Being denied sale of cigarettes due to one's age is associated with a reduction in smoking participation among girls. Instruction at school about the dangers of smoking is linked to a lower probability of smoking for all youths.

On smoking restrictions at school, the parameter on having a student ban is insignificant or has a positive association (for females). The most likely interpretation is that schools with smoking bans often have a larger share of student smokers. In fact, schools often adopt such policies if smoking among the student body becomes a persistent or increasing problem, leading to a positive correlation between smoking bans and smoking among students. A second potential explanation may be that having a ban may not reduce participation rates unless schools enforce such bans comprehensively. The inclusion of an interaction between the enforcement variable and the student ban variable suggests the effect is zero. This finding should not be surprising, as our variables are likely capturing the effect of both policy selection—where schools with a large share of smokers are more likely to adopt smoking bans among students—and the effect of the ban. Since policy selection is positively associated with smoking participation, and bans and enforcement of such bans are negatively associated with smoking, the net effect is comprised of these two opposing effects, which could explain our null findings. A ban on smoking for personnel is associated with reductions in the probability that girls smoke (significant at the 10% level). Adding the interaction of enforcement and smoking bans for personnel does not affect the decision to smoke among youth.

Average consumption among smokers

Table 3 outlines the results for average consumption among smokers. In general, the results indicate that smoking by personnel may be associated with increased consumption among girls but not among boys. A 10-percentage point increase in smoking by personnel is associated with a 6.8% increase in smoking among females, the equivalent of three more cigarettes a month.

Table 3

Generalised linear model of average consumption among smokers

A ban on smoking on school grounds among personnel is associated with reduced cigarette consumption among females. The student ban estimate is positive, consistent with the idea that schools that adopt smoking bans for students have more smoking among their students to begin with. The estimate on enforcing a student ban has the expected negative sign but is only significant for females (at 10% level). Similarly, the estimate for enforcing a staff ban is either insignificant or has a positive sign. Two reasons likely explain this finding. First, staff may be more likely to self-enforce and follow school policy. Second, the enforcement variable was aggregated from a single question that asked school personnel whether their school enforced the ban on smoking on school grounds for students and school personnel. Because the enforcement variable appears both as an interaction with a student ban and a staff ban, it is likely that little additional variation remains to predict the outcome, which can explain the insignificant results.v

The other controls have the expected signs. Exposure to cigarette advertising is associated with increased consumption among girls, and exposure to antismoking media reduces consumption among all youths. School instructions on the dangers of smoking and reduced access to cigarettes are linked to lower consumption among girls.

Teacher smoking and effects by personnel/teacher age and gender

The previous analysis does not differentiate between teacher smoking and smoking by administrative staff. To estimate the association between teacher smoking and youth smoking, we replace school personnel smoking with a variable that captures the share of teachers who smoked on school grounds in the past year. Furthermore, while our findings thus far suggest a link between school personnel and adolescent smoking, there is likely heterogeneity in response by personnel/teacher characteristics. Theoretically, behaviours of same-gendered personnel/teachers and personnel/teachers closer to one's age should be more influential since social ties with these groups exhibit a smaller social distance given that these ties do not need to bridge over age and gender.35 To test these hypotheses, we replace our personnel/teacher smoking variables with gender-specific and age-group-specific variables. For the age groups we use two mutually exclusive categories (1) personnel/teachers in their 30s and younger and (2) personnel/teachers in their 40s and older.

The results appear in tables 4 and 5. All results include the full set of controls used in the baseline model. We find that a 10-percentage point increase in smoking by teachers is associated with a 0.47 percentage point increase in adolescent smoking prevalence. Smoking among teachers is linked to a 7.4% increase in average consumption among female smokers, and a 2.8% increase in smoking among male smokers (significant at the 10% level). The effects largely mirror the findings for personnel but the prevalence estimates appear to be slightly smaller.

Table 4

The effect of teacher smoking, and personnel/teachers by gender and age on smoking participation

Table 5

The effect of teacher smoking, and personnel/teachers by gender and age on average consumption

The estimates by personnel/teacher gender suggest that smoking among female personnel and teachers affects average consumption among female smokers. The results by personnel/teacher gender suggest no effect for boys. The results only partially confirm theoretical predictions. While we find some of the expected theoretical relationships between female personnel and teacher smoking and consumption among female smokers, smoking by school personnel of any gender appears to affect smoking prevalence among female youths, and likewise smoking by male teachers appears to affect smoking prevalence among females.

We find that smoking by personnel and teachers in their 20s and 30s is associated with increases in smoking prevalence among all students and consumption among girls. Older personnel do not appear to affect smoking prevalence, but smoking among older personnel is associated with increased average consumption for girls (significant at 10% level). Smoking among older teachers is associated with increased likelihood of smoking participation among females. The relative effect of teachers in their 20s and 30s is slightly greater than that of older personnel and teachers, even though the point estimates between the two age groups are not statistically different from one another.

Discussion

This paper estimates the association of smoking on school grounds by personnel/teachers and smoking restrictions at school on smoking among youth in countries that participated in the GYTS. Our findings suggest that low-income and middle-income countries may reduce smoking among young people by restricting staff smoking on school grounds.

While this analysis is the first to estimate such effects for youth in low-income and middle-income countries, several limitations exist. Given data with a more flexible structure, future research may wish to control for school selection by employing school fixed effects. Additionally, future research may wish to identify instruments that will address personnel and student selection into schools, and identify the causal impact of personnel smoking on youth smoking.vi While the analysis measures the contemporaneous link between smoking by school personnel and youth smoking, research has not established the long-term effects of such socialisation, and future studies may wish to investigate these relationships. Finally, regarding our smoking ban and enforcement variables, we are cautious in interpreting these results as they do not represent actual policies and are likely noisy measures of true school-level policies. Much work remains in identifying the causal effect of such policies, which we hope future research will address.

We find that smoking among personnel and teachers on school grounds is associated with higher probability of smoking participation and higher average consumption among student smokers. These associations follow theoretically anticipated gender-specific effects, where smoking by female teachers/personnel has the greatest effect on smoking among female students. We also find that younger teachers/personnel may be more influential in determining smoking behaviours among young people.

What this paper adds

  • A large literature in developed countries finds that social influences have large impacts on smoking behaviours among young people, and that social and school environments can help shape these behaviours.

  • Despite the extensive evidence linking social environments and smoking in developed countries, little evidence exists on the impact of school environments and social influence on smoking among youth in low-income and middle-income countries.

  • This paper is the first to investigate the relationship between smoking among school personnel, and school-level policies and adolescent smoking behaviours in 62 low-income and middle-income countries.

  • We find that smoking on school grounds by personnel and teachers is associated with increased smoking prevalence among all youths, and conditional on smoking, higher consumption among young female smokers. We find some evidence that smoking by female personnel and teachers may stimulate smoking among female students, and that younger personnel and teachers may be more influential in determining smoking behaviours. We also find that smoking restrictions on staff may reduce average consumption among female students. We use an array of locality-specific tobacco characteristics and account for country-level unobservable factors in estimating the association between social influences on youth smoking.

References

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Supplementary materials

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    This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Footnotes

  • Contributors Both authors contributed in conceptualising and designing the study, and writing up the manuscript. SN completed the statistical analysis and tabulated results. FC provided feedback on the empirical framework.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • i Our data do not allow for the use of school fixed effects, or school level dummy variables. A school fixed effects methodology, the standard method to deal with selection when data structures allow, would require some form of variation in personnel smoking within schools (or over time) in order to identify the impact of smoking at school by school staff. Given the cross-sectional structure of the data there is no way to link schools over time. Furthermore, the data do not allow us to link average personnel smoking to individual classes within the school.

  • ii Each country has its own dummy or indicator variable in order to account for country-level unobservable factors that affect smoking and are correlated with our other control variables.

  • iii Individual-level responses to exposure variables (antismoking media and cigarette advertising) suffer from potential reverse causality, since smokers are likely targeted by both cigarette advertising and counter-advertising, and have a higher propensity to notice cigarette advertising than non-smokers.37 ,38 If we included smokers in generating exposure variables, these variables would represent both the effect of ‘targeting’ and the effect these policies have on smoking. Thus we define both variables among non-smokers in order to identify the effect of exposure and not that of targeting/selection.

  • iv We are aware of the concern in generating these variables from the survey data, as they likely also reflect knowledge/awareness of such bans in addition to the actual presence of a ban. However, we believe that leaving these variables out would produce bias in our personnel smoking variable. The bias would arise because we would not be able to separate the effect of personnel smoking, per se, from enforcement of policies by personnel who smoke. Leaving enforcement variables out would likely bias our estimates of personnel smoking. We therefore include these variables but caution that the estimates ought to be interpreted carefully and do not represent actual policy variables.

  • v To reduce concerns over high levels of collinearity affecting estimates, we conducted robustness checks where we residualised the staff enforcement variable. The procedure reduced the variance inflation factor to 1, and the results were identical to those presented in table 3.

  • vi We conducted additional analyses using instrumental variables, but the instruments were weak and the results could not be credibly interpreted.

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