Women’s Employment and Family Trajectories Shape Health Unequally in Later Life Across Welfare Regimes

• In Event: Disrupting Health Inequities Across the Life Course (Co-sponsored by Aging and the Life Course Section)

Abstract

This study investigates how female employment and family formation trajectories jointly intersect with welfare regimes to shape later life health outcomes. Existing studies have shown a number of associations between patterns of employment/family trajectories and health at older ages. However, it remains unclear whether the accumulation of social roles, such as employment or childbearing, translates into health benefits (i.e., role accumulation) or penalties for women (i.e., role strain). This study aims at establishing how the effects vary across welfare regimes using a machine learning-based g-computation framework. We estimate the average treatment effects (ATEs) of counterfactually following typical employment and family trajectories, identified through sequence and clustering analysis, across Scandinavian, Mediterranean, and Corporatist welfare regimes. By comparing ATEs across regimes, we capture how life-course processes interact with institutional contexts to produce heterogeneous health outcomes in women's later life. A central finding is that health advantages and disadvantages are not inherent to specific roles or trajectories themselves, but are shaped by how these trajectories are organized and supported within their institutional context.Overall, the results highlight that the link between life-course trajectories and health is best understood when assessed within broader institutional structures that define, enable, and constrain women’s role combinations across the life course.

Authors

• Nanum Jeon, University of California, Los Angeles

• Aitor Garcia-Aguirre, Spanish Research Council, University Carlos III of Madrid

• Wesley Wang, Purdue University

• Yanji Du, University of California-Los Angeles

• Bruno Arpino, Department of Statistical Sciences, University of Padua

• Linda Vecgaile, Max Planck Institute for Demographic Research (MPIDR)

• Emilio Zagheni, Max Planck Institute for Demographic Research