Peer Networks and Tobacco Consumption in South Africa

Abstract

This paper deepens the empirical analysis of peer networks by considering their effects on both smoking participation and smoking intensity in South Africa, a country where majority initiate smoking at adolescent age. Peer networks are key in determining the smoking behaviour of youths, but the magnitude of the effects is still debated, questioned and inconclusive. I used a control function approach, a two‐stage least square and the fixed effect method to address the potential endogeneity of peer network. The results suggest positive and significant peer effects on smoking participation and smoking intensity. While the network effects are consistently positive and significant, the magnitude of the estimates varies across methodological approaches with the instrument variable estimates generally lower. Including older adults in the peer reference group increases the peer effect estimates. Finally, using clusters as an alternative measure of network size in wave one, I show that peer effects are independent on network size but rather on network quality. Relative to the results of this paper, previous literature has documented larger peer effects on the decision to smoke. The findings suggest that policies (excise tax) that directly affect the decision to smoke and the smoking intensity of the peer reference group are likely to affect own smoking behaviour.

Social networks and technology adoption: Evidence from mobile money in Uganda

“…In the absence of such rich data, simplifications have been made. One of these is the use of geographical and cultural proximity as a proxy for social group (Bertrand et al, 2000;Burns et al, 2010;Mukong, 2017). The assumption is that the behaviour of individual i is a function of the average behaviour of his/her neighbours n i �…”

“…It is evident that social networks can be categorized in dimensions of race, ethnicity, age, education and religion (Albeck & Kaydar, 2002). Consequently, Arai (2007) and Mukong (2017) showed that besides relatives, other forms of socialization, including education attainment, are important determinants of fertility and substance abuse respectively. We use age-educational attainment and geography (district of residence) to construct a continuous variable as a measure of network (see footnote 7).…”

“…This suggests that the source of awareness or social ties could be an important determinant of mobile money adoption. While social ties and peer effects have been shown to significantly influence most individual and economic outcomes (Angrist, 2014;Dahl et al, 2014;Maertens & Barrett, 2012;Mukong, 2017), little is known about their effect on mobile money technology adoption. Economists suggest that many individual decisions, including labour market decisions (Angrist, 2014;Burns et al, 2010), education attainment (Burke & Sass, 2013), welfare participation (Bertrand et al, 2000;Dahl et al, 2014), health and health-care use (Deri, 2005;Fortin & Yazbeck, 2015;A.…”

“…Economists suggest that many individual decisions, including labour market decisions (Angrist, 2014;Burns et al, 2010), education attainment (Burke & Sass, 2013), welfare participation (Bertrand et al, 2000;Dahl et al, 2014), health and health-care use (Deri, 2005;Fortin & Yazbeck, 2015;A. K. Mukong & Burns, 2015), substance use (McVicar & Polanski, 2014;Mukong, 2017), internet adoption (Lu et al, 2005) and agricultural technology adoption (Bandiera & Rasul, 2006;Maertens & Barrett, 2012) are positively correlated with the behaviour of the social group the individual belongs to. This paper contributes to this literature by investigating the effect of social networks on mobile money technology adoption.…”