Regional Inequalities in Consumption Patterns: A Multilevel Approach to the Case of Italy

The main aim of this paper is to evaluate the disparities in the Italian regions on the demand side. In more detail, an attempt will be made to find if the consumption behaviour of Italian households is different in the regions. With this in mind, Istat's 2000 Italian Family Budget data set was analysed. The data in question, which were collected through a two‐stage sample over Italy's 20 regions, contains information regarding the expenses of approximately 23,000 households. In this analysis, both households and regions are considered as units: households are nested in the regions so that the basic data structure is hierarchical. In order to take this hierarchical structure into account, a multilevel model was used, making it possible for parameters to vary randomly from region to region. The model in question also made it possible to consider heterogeneity across different groups (regions), such as stochastic variation. First, regional inequalities were tested using a simple model in which households constituted the first level of analysis and were grouped according to their region (the second level). As a second step, and in order to investigate the interaction between geographical context and income distribution, another model was used. This was cross‐classified by income and regions. The most relevant results showed that there is wide fragmentation of consumption behaviour and, at the same time, various differentiated types of behaviour in the regions under analysis. These territorial differentials become clear from income class and items of consumption. L'objet du travail est l'analyse des différences, entre les régions italiennes, des comportements des consommateurs. Le traitement statistique des données individuelles est originale car il est conduit par un modèle ‘multilevel’. Le modèle multilevel tient compte de la structure hiérarchique de données et permet au paramètres estimée de varier par hasard. En outre, ce modèle permet que l'hétérogénéité entre le différent groupes de familles (les unités statistiques) peut varier par hasard entre le régions. Pour l'analyse des différences régionales modèle ‘multilevel nous avons estimée un premier modèle avec les familles au premier dégrée hiérarchique et les régions au second dégrée. Car le facteur géographique interagit avec la distribution du revenue dans chaque région nous avons estimée un autre modèle cross‐classifiée par lequel le familles sont groupées par le revenue.