Retirement, family transfers, and future generations: new insights into intergenerational effects with causal machine learning
While retirement research has primarily examined the effects on health, wellbeing, income, and household finances, this proposed research argues that retirement may also have broader intergenerational consequences that remain largely unexplored. To address this gap, the research will investigate whether retirement influences time and financial transfers between older and younger generations within UK families. Additionally, it will explore whether retirement, through its impact on instrumental and financial support, has spillover effects on the economic and subjective wellbeing of future generations.
This research moves beyond conventional approaches by employing advanced causal machine learning techniques – including causal forests, double-debiased machine learning, and meta-learners – to enhance causal inference and better address confounding factors, selection bias, and non-linearity. By combining the predictive power of machine learning with the strengths of traditional econometrics, these innovative methods could provide a more nuanced analysis of retirement outcomes, offering deeper insights into the complex relationships between retirement, intergenerational transfers, and their social and economic effects on retirees and their children.
By exploring previously overlooked consequences of retirement and pioneering the use of causal machine learning in studying retirement and intergenerational effects, this research will make a significant contribution to the literature. The findings will also inform pension and social care policies by providing evidence on retirement’s wider, long-term societal effects and the implications of extending working lives. Understanding how retirement shapes family support networks and impacts the wellbeing of future generations is crucial for guiding policy decisions, particularly amid ongoing uncertainty over future pension reforms.
Read more about Cigdem’s work on her university profile page.
