Wealth, Inequality, and Life Satisfaction: The Impact of Economic Changes across Age Groups and Generations in Europe

Comparative studies usually indicate a positive relationship between GDP per capita and life satisfaction and a negative relationship between the Gini coefficient and life satisfaction. These associations are also detected in longitudinal, cross-sectional, or panel studies conducted within individual countries. However, comparative and longitudinal studies examining contextual variables' effects across countries and over time on the life satisfaction of different population segments are rare. Many studies have identified that life satisfaction varies over the life course (U-shaped age effect), between different cohorts (generation effect), and the effect of time (a period effect) is usually analysed to capture the changes in a country or region's social, economic, or political context. Using data from five waves of the European Social Survey (2016–2024), covering three recent crises (the migrant crisis, the COVID-19 pandemic, and the war in Ukraine), we will examine how a) contextual differences between societies and b) changes within societies over time affect the life satisfaction of individuals across different ages and generations in Europe. We assume that differences between European countries and changes within countries over time in GDP per capita, Purchasing Power Standard, and the Gini coefficient affect different age groups and generations in distinct ways. We also assume that these effects vary within age groups and generations depending on their position in the labour market (employment, unemployment, inactivity, type of contract) and their material standard.

To conduct the analysis, we will use a multilevel approach with three levels: the first consists of individuals with their sociodemographic characteristics, the second nests individuals within cohorts (generations) and periods, and the third nests them within countries. Building on the recent insights of Giesselmann and Schmidt-Catran (2019) and the application of similar data by Czymara (2021), we will attempt to disentangle contextual effects across countries and over time.