Measuring Individual Material Well-Being Using Multidimensional Indices: An Application Using the Gender and Generation Survey for Russia

This paper suggests a new and comprehensive approach to the assessment of the material well-being at the individual level by constructing a multidimensional index. Using this approach, material well-being is understood as a generic notion that covers a number of different domains, whereas the concept of domain is used to distinguish between different aspects of people’s resources, including income security, basic needs, durables, housing and subjective material well-being. Each dimension is measured independently, using the best indicators available, to generate a score or domain index for each aspect of material well-being. The procedure of re-weighting the indicators within the separate domains enables us to account for the disparity in resources and consumer preferences across different population subgroups. The final domain scores, combined with explicit weighting, are then used to generate a summary material well-being index. The domain indices and the summary material well-being index are validated by exploring their relationships to key socio-economic attributes, which were previously shown to be strongly associated with individual material well-being. The results showed that the summary indices of material well-being are characterized by greater differentiation in relation to such measures, as occupational class and judgments of satisfaction with one’s life. This allows us to conclude that our summary indices capture the latent concept of material well-being better than any of our domain indices used separately. Although the index is constructed using the Russian Gender and Generation Survey data for 2007, the methodological approach that we applied can be easily replicated in other surveys which contain information on several aspects of material well-being

Between-class earnings inequality in 30 European countries: a regression-based decomposition

This article studies earnings inequality between social classes across 30 European countries. Class inequality in earnings is found across the board although there are some exceptions. However, the degree of class inequality varies strongly across countries being larger in Western and Southern European countries and smaller in Eastern and Northern European countries. Furthermore, we find that differences in class composition in terms of observed characteristics associated with earnings account for a substantial proportion of these between-class differences. Differences between classes in the returns to education and other characteristics play less of a role. In all these respects there is a sizeable cross-national variation. This points to important differences between countries in how earnings are structured by social class

AITIA: embedded AI techniques for industrial applications

Computer Science