Tracing the cigarette epidemic: An age-period-cohort study of education, gender and smoking using a pseudo-panel approach

Abstract

(online), with the title: Tracing the cigarette epidemic: An age-period-cohort study of education, gender and smoking using a pseudo-panel approach http://www.sciencedirect.com/science/article/pii/S0049089X14001033Tracing the cigarette epidemic: An age-period-cohort study of education, gender and smoking using a pseudopanel approach AbstractThis study examined if temporal variations in daily cigarette smoking and never-smoking among groups with different levels of education fit the pattern proposed by the theory of diffusion of innovations (TDI), while taking into account the separate effects of age, period and birth cohort (APC). Aggregated data from nationally representative interview surveys from Norway from 1976 to 2010 was used to calculate probabilities of smoking using an APC approach in which the period variable was normalized to pick up short term cyclical effects. Results showed that educational differences in smoking over time were more strongly determined by birth cohort membership than variations in smoking behavior across the life course. The probability of daily smoking decreased faster across cohorts among higher compared to lower educated. In contrast, the change in probability of never having smoked across cohorts was similar in the two education groups, but stronger among men compared to women. Moreover, educational differences in both daily and never-smoking increased among early cohorts and leveled off among late cohorts. The results emphasizes the importance of birth cohort for social change and are consistent with TDI, which posits that smoking behavior diffuse through the social structure over time.

Age-Period-Cohort Analysis of Smoking Prevalence

“…However, recently, several studies have attempted to perform APC analysis of smoking prevalence by combining crosssectional data for several years [20][21][22]. Veday [20] performed APC analysis of smoking prevalence in accordance with education level and gender and explained smoking prevalence in terms of social changes associated with each birth cohort and the theory of diffusion of innovations. Moreover, Veday [20] suggested that smoking prevalence declined with increasing education level via intercohort diffusion.…”

The role of demographic and behavioural change for the long-term decline in daily smoking in Norway

“…However, this scenario assumes that smoking behaviours among 85 different age groups are stable. Given that smoking prevalence has decreased among each consecutive birth cohort over the last decades in most developed countries, 12,13 and that smoking has become more prevalent among older compared with younger birth cohorts in some countries, 19 it is possible that population ageing could compensate for decreasing smoking prevalence among younger age groups.…”

“…10,11 From a macrosocial point of view, long-term variations in smoking can be a result of people changing their behaviour because they grow older (age effects) and/or because new birth cohorts, with different smoking behaviours, enter the social system (cohort effects). 12,13 In addition, events at certain points in time may affect smoking among all groups, for example changes in taxation or new restrictions on where to smoke (period effects). 14,15 However, long-term variation in smoking prevalence may also result from changes in the demographic composition of a population (demographic effects).…”

The Smoking Epidemic Across Generations, Gender and Educational Groups: A Matter of Diffusion of Innovations

“…We address the identification problem by employing a solution from Deaton (1985), where temporal change is decomposed into birth-cohort dummies and a continuous age profile, while period effects are regarded as exogenous shocks that sum to zero in the long run. Based on Bell and Jones (2013) and Veday (2014), we assume that changes in smoking habits over time is the result of birth cohorts' differing attitudes toward smoking, rather than period effects, and allocate temporal trends to variations in age or cohort. (See also Veday, 2014.)…”