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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
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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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2021-2023 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.