Abstract

The problem of mutual influence between the levels of local community welfare and individual well-being (including measures of subjective well-being) - which is interesting from the point of view of local development policies - poses several challenges to public statistics and to the task of selection the appropriate, problem-specific analytical strategy. The first concerns the availability of spatial data from various sources (administrative and survey) – such as used in this study: Local Data Bank, EU-SILC, Time Use Survey, Social Diagnosis, Social Cohesion Survey – which have been integrated in a multi-source analytical database. using a territorial code as ‘integrator’. The latter includes a review of available models motivated by the need to select a specific (relatively ‘best’) analytical strategy, making the selection criterion - formally its methodological effectiveness - dependent on substantively important aspects of the modeled interaction. Hence, the 'evaluation' of each of the rich set of approaches demonstrated in the empirical part of this study - from descriptive ones, such as Benefit Incident Analysis (in the Marginal BIA version) and Correspondence Analysis through Mahalanobis Distance and Compensating Variations to the analysis of spatial dependence and multilevel spatial modeling - is supplemented with the specification of the included to the appropriate equations contextual factors which are (hypothetically) modifying the modeled relationship between individual and community measures of well-being. Such as the profile of local risk factors, the presence (activity) of the third sector entities (NGOs and 'social capital'), homogeneity-heterogeneity of the community (including differences in the wealth of residents), etc. In summary, an analytical framework is proposed in the form of a matrix approach to the types of determinants of interaction (taken into account in the appropriate hypotheses) and the types of analytical strategies suitable for its modeling in specific problem contexts.