Divorce and Diagnosis – Register-based Analyses of the Impact of Divorce on Health and Employment Trajectories (DiDi)
Content and Objectives
The DiDi research project, funded by the German Research Foundation (DFG), investigates the effects of divorce on health and employment trajectories based on a life course approach. Health is operationalised in particular through the use of rehabilitation services and through deaths. A large number of studies have shown that family transitions – such as the birth of a child, marriage, divorce or separation – have a significant impact on health and well-being. However, previous studies have been limited in some respects by the disciplinary focus of the research. Sociological studies have mostly used life satisfaction or subjective health status as outcome variables, while demographic and epidemiological studies have often used very limited batteries to operationalise family behaviour. The aim of this project is to close gaps in previous research by using register data from the German Pension Insurance, which contains detailed information on health status (classified according to the International Classification of Diseases (ICD-10)) as well as detailed employment and partnership biographies. The DiDi project investigates: a) the influence of divorce on disease diagnoses (i.e. mental and physical diseases as well as mortality); b) the influence of disease diagnoses of divorced individuals on labour market transitions, and c) the moderating role of age, gender and past family behaviour (i.e. primarily the male breadwinner model). This multidisciplinary project, which started in April 2025, is being carried out in collaboration with the Hertie School of Governance and Charité - Universitätsmedizin Berlin with support from the Research Data Centre of the German Pension Insurance (FDZ-DRV). The research group "Mortality" is responsible for the module "Divorce and Death," which examines the influence of divorce on mortality risk.
Data and Methods
Several microdata sets from the German Pension Insurance (Aktiv-Versicherten-Statistik AKVS, Reha-Statistik-Datenbasis RSD, Versorgungsausgleichsstatistik VA and Rentenbestand/-wegfall) as well as survey data (Sozio-oekonomisches Panel SOEP-RV and German Family Panel pairfam) will be used. Event history models and generalised linear models (e.g. Poisson models) are used to assess mortality risks. Employment status, income and time since divorce are the most important independent variables in this process. The data also provide insights into cause-specific mortality.
Duration
since 04/2025-03/2028
Partners
- Prof. Dr. Michaela Kreyenfeld, Hertie School, Berlin, Germany
- Prof. Dr. Paul Gellart, Charité – Universitätsmedizin Berlin, Germany