Machine-Learning Text Analysis of Intergenerational Mobility Perceptions in Germany, Sweden, and the United Kingdom

Research on intergenerational mobility has traditionally focused on objective markers of socioeconomic position. In this study, we argue that the subjective aspects of intergenerational mobility deserve greater attention and empirically explore what individuals report they compare when they gauge their intergenerational mobility trajectories. Drawing on representative survey data from Germany, Sweden, and the United Kingdom, as well as machine-learning-driven text analyses of open-ended survey responses, we reveal that, in addition to conventional measures of intergenerational mobility, such as education, occupational status, and income, individuals consider a diverse array of factors, including family life, home ownership, and lifestyle choices. Our findings highlight the heterogeneity of these comparisons across different countries, genders, and generations. We identify significant variations in the dimensions of intergenerational comparisons, such as the prominence of education in Sweden, the focus on housing in the United Kingdom, and the salience of freedom, opportunity, and lifestyle in Germany. Furthermore, gender differences reveal that females are more likely to emphasize education and family life, while males focus on income and occupational status. These insights provide a deeper understanding of the subjective dimensions of intergenerational mobility and contribute to ongoing debates in social stratification research and general social theory.