Measuring Peer Effects on Youth Smoking Behavior

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThis paper examines the role of peer effects in smoking behavior using data of middle and high school students in the United States. I present a random utility model that explicitly incorporates complementarity between individual and peer smokings. A Markov process model of smoking interactions between individuals is presented, under the assumption that such interactions occur frequently. I estimate the structural parameters of the model using a steady state distribution that is uniquely determined by the Markov process . The empirical results strongly support the presence of positive peer effects in smoking behavior among young people. Interestingly, peer interactions are found to be stronger within the same gender than across genders. The same result is found for race. Moreover, a multiplier effect is found. The impact of a tax on youth smoking increases by a factor of 1.5 when peer interactions are present. JFL Classification: C15, C35, C63, I12

Computationally intelligent agents in economics and finance

“…14 With moderate degrees of simplifying assumptions on individuals' decision models as well as interaction mechanisms, this network-based agent-based model can be parameterized and estimated as an econometric model. This is basically what was done in [46], which estimated the interaction mechanism among young people in relation to smoking behavior by using the result of [2]. 15 Its empirical results strongly support the presence of positive peer effects in smoking behavior among young people.…”

“…[46] can be regarded as an outcome of the new research trend that embeds the conventional discrete choice models, also known as the qualitative response models, in a social network, and examines the impact of the social interaction upon individuals' discrete choices. 14 With moderate degrees of simplifying assumptions on individuals' decision models as well as interaction mechanisms, this network-based agent-based model can be parameterized and estimated as an econometric model.…”

Peer and Selection Effects on Youth Smoking in California

“…A 25 percentage point increase in schoolmate smoking is associated with a 4.0-24.2 percentage point increase in own smoking. The pattern of higher Gaviria and Raphael (2001) Naive (LPM) 4.0% (school) IV (2SLS LPM) 3.9% Kooreman and Soetevent (2006) Structural, no fixed effect 6.2% (classroom) Structural, school fixed effect .8% Lloyd-Richardson et al (2002) Naive (logit) 28.4% (three best friends) Nakajima (2004) Structural, no fixed effect 8.6% (school) Structural, county fixed effect 7.2% Norton et al (1998) Naive (probit) 25.4-54.5% (neighborhood) IV (two-stage probit) 26.4-86.0% Norton et al (2003) Naive (LPM) 13.2% (three best friends) Naive (LPM) 9.9% (school) Powell et al (2005) Naive (probit) 14.5% (school) IV (AGLS probit) 14. 4% Wang et al (1995) Naive ( reduced-form estimates of close-friend influence than schoolmate influence may reflect an actual difference in strength of influence, or it may be an artifact of the larger simultaneity bias in reduced-form estimators when the peer group is small.…”

“…Krauth (2005) provided a more detailed empirical application using 1994 Canadian data with province identifiers. Other articles estimating equilibrium models of youth smoking include Nakajima (2004) and Kooreman and Soetevent (2006). Both of these articles differ from the current article and from Krauth (2005) in that they estimate classroom level peer effects rather than close-friend effects and they use only aggregate level fixed effects (at the school level for Kooreman and Soetevent, and the county level for Nakajima) to account for nonrandom peer selection.…”

Quitting and peer effects at work

“…This is particularly the case when thinking about quitting addictive behavior. For instance, evidence suggests that it may be easier to stop smoking when other members of the reference group stop smoking (Nakajima 2007).…”