Sorting into Secondary Education and Peer Effects in Youth Smoking

Pertold, Filip

doi:10.2139/ssrn.1517222

Cited by 4 publications

(9 citation statements)

References 16 publications

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“…Overall, the results verify findings from the existing literature in the US and emerging studies from Europe that peer smoking is an important determinant of smoking among young people (McVicar and Polanski, 2014;Clark and Loheac, 2007;Lundborg, 2006;Ali and Dwyer, 2009;Fletcher, 2010;McVicar, 2011;Pertold 2009). Previous studies have found that a 10 percentage point increase in peer smoking is linked to increases in individual smoking of between 1 and 5.8 percentage points.…”

Section: Discussionsupporting

confidence: 86%

“…Overall, naïve OLS estimates suggest that higher smoking prevalence among class peers is associated with higher smoking prevalence for the individual. Moreover, these OLS estimates are in line with findings from previous studies in Europe (Lundborg, 2006;McVicar, 2011;Pertold, 2009). …”

Section: Instruments and Identifying Assumptionssupporting

confidence: 91%

“…A 10 percentage point increase in peer smoking increases youth smoking between 0.9 to 5.8 percentage points. Evidence from Europe has also found peer influence to be one of the primary drivers of youth smoking, though the evidence is more limited here (Lundborg, 2006;McVicar, 2011;Pertold, 2009). …”

Section: Introductionmentioning

confidence: 74%

“…al. 2005;Gaviria and Raphael, 2001;Clark and Loheac, 2007;Fletcher, 2010;Pertold, 2009;McVicar, 2011). Nonetheless, little is known about the effect of peer influences on smoking in most countries.…”

Section: Introductionmentioning

confidence: 99%

“…To address the correlated effects problem I follow the current literature and control for school fixed-effects (McVicar and Polanski, 2014;Clark and Loheac, 2007;Lundborg, 2006;Ali and Dwyer, 2009;Fletcher, 2010;Pertold, 2009). School fixed-effects account for unobservable school-level characteristics that are common to all students in a school, such as school smoking policies, school resources spent on tobacco control programmes, and common background characteristics at the school-level.…”

Section: Introductionmentioning

confidence: 99%

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Peer Effects and Youth Smoking in the European Global Youth Tobacco Survey

Nikaj

2017

Review of Economic Perspectives

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This paper investigates the effect of peer smoking on individual smoking among youths in 10 countries that participated in the European Global Youth Tobacco Survey (GYTS). I control for endogeneity in school selection and unobserved schoollevel characteristics through the use of school fixed-effects. I use instrumental variables to address the simultaneity in peer and individual behaviours. Identification arises by comparing students in different classes within the same school. On average, an increase in the share of classmates who smoke by 10 percentage points increases the probability that an individual in that class will smoke by 3 to 6.9 percentage points. The results imply that any policy intervention such as anti-smoking messages, smoking bans, or higher cigarette prices will be even more cost-effective because of the social multiplier effect of peers -policies affecting some individuals in a group will generate spillovers to others through the peer effect.

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Section: Discussionsupporting

confidence: 86%

Section: Instruments and Identifying Assumptionssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Peer Effects and Youth Smoking in the European Global Youth Tobacco Survey

Nikaj

2017

Review of Economic Perspectives

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Smoking Czechs: Modelling Tobacco Consumption and Taxation

Janda¹,

Štrobl²

2019

Prague Economic Papers

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We model the future tobacco consumption, size of smoking population and governmental tax revenues in the Czech Republic. The main model assumption states that smokers determine their future tobacco consumption behaviour as adolescents. Further assumptions make the model applicable to the data from the Czech National Monitoring Centre for Drugs and Drug Addiction. Future teenage smoking rates and average consumption are the inputs to the model; consumption growth coefficients for each age category are estimated using zero-inflated negative binomial regression. Several scenarios are built to model possible developments, including extreme cases. All our scenarios show that all model outcomes are going to grow until 2028 in a very similar pattern. In particular, the projected number of smokers in 2028 is by 4-8% higher than in 2013, the total daily tobacco consumption and tax revenue by 7-26%. This increase is induced by aging of large birth cohorts.

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