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In the last years, the analysis of the demand for addictive goods has received renewed and increasing interest. Since Becker and Murphy’s (1998) fundamental contribution, theoretical and empirical studies have produced a large literature on the price and nonprice determinants of alcohol and tobacco demand. Theoretical and empirical studies on alcohol and tobacco consumption have drawn attention to two topics. On the one hand, one strand of literature has focused on the dynamics of addictive consumption and on the price responsiveness of the demand for addictive goods, analysing the intertemporal decisions of either myopic or far-sighted rational individuals. Empirical research on this topic has addressed the issues connected to the application of Becker and Murphy’s (1988) rational addiction model, both with aggregated (Becker et al., 1994) and disaggregated (Chaloupka, 1991; Baltagi and Griffin, 1995, 2001; Baltagi and Geishecker, 2006) data. On the other hand, the growing availability of microdata at a high disaggregated level has allowed to model the censoring nature of alcohol and tobacco consumption, accounting for zero observations and simultaneously exploiting the richness of survey data information to control for heterogeneous individual (or household) behaviour (Jones, 1989; Blaylock and Blisard, 1992, 1993; Garcia and Labeaga, 1996; Yenand Jones, 1996). From a policy perspective, cross-sectional surveys enables to improve the knowledge of the impacts of demographic and socio-economic variables on alcohol and tobacco expenditure and help the design of public health programs to achieve drinking and smoking-reduction objectives. Recent developments in the analysis of addictive goods have followed three main directions. Firstly, some authors (Jimenez-Martin et al., 1998; Labeaga, 1999; Jones and Labeaga, 2003), using genuine and/or pseudo panel data, have tried to unify the two above-mentioned approaches, by explicitly dealing with the issues of measurement errors, unobserved individual heterogeneity and censoring in rational or myopic models of addiction. Secondly, the case of multiple addictive goods has been taken into account to analyze, together with own consumption dynamics, both intra-temporal and intertemporal interactions between goods. In particular, in the context of intertemporal analysis of addiction, it is worth remarking the works of Bask and Melkersson (2004), Pierani and Tiezzi (2005) and Fanelli and Mazzocchi (2006), that extend the rational habit formation model to consider the case of two addictive goods. Finally, following the works of Manski (1993, 1995), several studies have emphasized the importance of social interactions and peer effects on smoking and drinking decisions (Auld, 2005; Krauth, 2005; Powell et al., 2005). Social interactions are widely regarded as important determinants of many behavioural and economic outcomes, based on the idea that the utility that an individual receives from doing a certain activity depends on the actions of the other individuals in the person’s reference group (Becker, 1996; Brock and Durlauf, 2001). In particular, the point at issue is to verify whether the average smoking or drinking behaviour in a group affects the behaviour of the individuals in that particular group

Essays on the demand for addictive goods

ARISTEI, David
2007-01-01

Abstract

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addictive goods
In the last years, the analysis of the demand for addictive goods has received renewed and increasing interest. Since Becker and Murphy’s (1998) fundamental contribution, theoretical and empirical studies have produced a large literature on the price and nonprice determinants of alcohol and tobacco demand. Theoretical and empirical studies on alcohol and tobacco consumption have drawn attention to two topics. On the one hand, one strand of literature has focused on the dynamics of addictive consumption and on the price responsiveness of the demand for addictive goods, analysing the intertemporal decisions of either myopic or far-sighted rational individuals. Empirical research on this topic has addressed the issues connected to the application of Becker and Murphy’s (1988) rational addiction model, both with aggregated (Becker et al., 1994) and disaggregated (Chaloupka, 1991; Baltagi and Griffin, 1995, 2001; Baltagi and Geishecker, 2006) data. On the other hand, the growing availability of microdata at a high disaggregated level has allowed to model the censoring nature of alcohol and tobacco consumption, accounting for zero observations and simultaneously exploiting the richness of survey data information to control for heterogeneous individual (or household) behaviour (Jones, 1989; Blaylock and Blisard, 1992, 1993; Garcia and Labeaga, 1996; Yenand Jones, 1996). From a policy perspective, cross-sectional surveys enables to improve the knowledge of the impacts of demographic and socio-economic variables on alcohol and tobacco expenditure and help the design of public health programs to achieve drinking and smoking-reduction objectives. Recent developments in the analysis of addictive goods have followed three main directions. Firstly, some authors (Jimenez-Martin et al., 1998; Labeaga, 1999; Jones and Labeaga, 2003), using genuine and/or pseudo panel data, have tried to unify the two above-mentioned approaches, by explicitly dealing with the issues of measurement errors, unobserved individual heterogeneity and censoring in rational or myopic models of addiction. Secondly, the case of multiple addictive goods has been taken into account to analyze, together with own consumption dynamics, both intra-temporal and intertemporal interactions between goods. In particular, in the context of intertemporal analysis of addiction, it is worth remarking the works of Bask and Melkersson (2004), Pierani and Tiezzi (2005) and Fanelli and Mazzocchi (2006), that extend the rational habit formation model to consider the case of two addictive goods. Finally, following the works of Manski (1993, 1995), several studies have emphasized the importance of social interactions and peer effects on smoking and drinking decisions (Auld, 2005; Krauth, 2005; Powell et al., 2005). Social interactions are widely regarded as important determinants of many behavioural and economic outcomes, based on the idea that the utility that an individual receives from doing a certain activity depends on the actions of the other individuals in the person’s reference group (Becker, 1996; Brock and Durlauf, 2001). In particular, the point at issue is to verify whether the average smoking or drinking behaviour in a group affects the behaviour of the individuals in that particular group
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/337849
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