The project focuses on contextual factors explaining differences in young adults’ life chances in a longitudinal perspective. By life chances we mean the structural contexts influencing choices and behavior with consequences for education, labor market situation, health, and criminality. Such life chances are strongly and systematically influenced by social class, gender, and ethnicity.

The dominant ways to study these differences are to focus on socialization effects in the family and differences in human capital more generally. These approaches have been highly successful, but there is still a considerable part of it left unexplained by these models. Part of the reason for this is that they have taken contextual factors insufficiently into account. We use a synthetic approach to the study of life chances that integrates the traditional models with a fuller focus on contextual factors — neigborhoods and social networks in particular.

Our goal: to reach interpretative explanations

The aims are to arrive at better specified models that will more accurately predict differences in outcomes, and to reach beyond prediction and to identify generative mechanisms causing the observed associations between explanans and explandum. The goal is to reach what Max Weber calls interpretative explanations, and in doing so we need to specify the sociologically relevant settings in which people find themselves.

Different methods to reach better understanding

This social mechanism based approach to life chances necessitates methodological pluralism, in which quantitative and qualitative methodological techniques are combined. The project analyzes both large-scale random samples in order to generalize findings and does qualitative analyses of strategically selected small-n case studies in order to identify social mechanisms and understand the ways in which they operate in practice.

Combined, the different methodological approaches allow us to deal with complexity and strike a balance between the subjectively unique in qualitative analysis and quantitatively generalized findings.