Jan Einhoff
PhD Candidate
DYNAMICS Research Training Group
Humboldt University & Hertie School
Bluesky
GitHub
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I am a PhD Candidate at the DYNAMICS Research Training Group, a joint programme of Humboldt University and the Hertie School in Berlin, funded by the German Research Foundation (DFG). During my PhD, I was a visiting researcher at the Finnish Centre for Pensions in Helsinki, the University of Wisconsin in Madison, the Max Planck Institute for Demographic Research in Rostock, and Nuffield College in Oxford.
My research centres on topics in sociology and social demography (life courses, population ageing, social stratification, social and labour market policy, among others) as well as novel quantitative methods for description and causal inference. For my dissertation, I investigate the class stratification of work-to-retirement transitions in the context of population ageing and extending working life policies in Europe. I use a wide range of quantitative methods, including advanced survival, sequence and decomposition analyses as well as causal machine learning approaches. I also like to work with the potential outcomes framework and graphical causal models.
In addition to my PhD research, I have worked as a Consultant at the OECD's Directorate for Science, Technology and Innovation, where I contributed to reviews of national innovation policy and a project exploring natural language processing tools and large language models for innovation policy analysis. In 2024, I was selected as a Young Thinker at the Centre for European Policy Studies (CEPS) to discuss policy priorities for the European elections.
Here are some of my current research projects:
1. Cohorts' working life expectancies and working years lost in 21 European countries[R&R] [Replication materials]
Details
Abstract: Across Europe, the extension of working lives has been a central policy goal for more than two decades. Working life expectancy (WLE) and working years lost (WYL) are well-suited demographic indicators for assessing countries’ progress towards achieving this goal. This article first reviews all available estimates of WLE and WYL for European countries. It then uses the largest available micro-level data – the European Labour Force Surveys (n > 10 million) – to estimate and project WLEs and WYL for cohorts of men and women aged 55–64 and 65–74 in 21 European countries. The results show that WLEs have generally increased, most rapidly in Central and Eastern Europe and in Western Europe. Northern European countries reach the highest levels of WLEs. However, country and gender differences remain large, especially when WLEs are adjusted for working hours. Correlational analyses suggest that working years have been gained primarily from successive cohorts losing fewer working years to retirement. The remaining WYL to retirement, to inactivity among women, and to unemployment in Southern Europe will be the main barriers to a further extension of working lives over the coming years.
[Under review] [Replication materials]
Details
Abstract: Despite its key role in generating and consolidating social inequalities, little is known about the role of housing at critical life course transitions, particularly in later life. This article studies the dynamic effect of home ownership on the risk of entering retirement between age 51 and 65 in two distinct institutional contexts. The analysis employs a novel causal inference approach and machine learning based on panel data from Germany and the UK. Home ownership is found to raise individuals’ retirement risks compared to renting by up to 21.3pp. in the UK and up to 7.4pp. in Germany. However, these effects are largest or only present for outright homeowners and close to salient retirement age thresholds. These findings highlight housing as a major yet under-recognized source of ‘property-based welfare’ and inequality in late working lives, and show how housing and welfare state institutions can mitigate or exacerbate this role.
[Work in progress]
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Abstract: Gender disparities in later-life employment participation are large and persistent in most European countries. While theory and prior empirical work suggest that older women’s lower employment participation is due to their disadvantaged occupational positions compared to men, the precise extent to which occupational gender segregation contributes to the observed disparity remains unclear. To quantify this effect, this article adopts and extends a recently proposed decomposition approach using debiased machine learning and large-scale survey data on men and women in Germany aged 55 to 64 (n = 379,001). Surprisingly and contrary to expectations, the contribution of occupational segregation is modest and even negative: under a gender-equal distribution of occupations, the disparity would be 18\% larger. Similar results are found for East and West Germany, across education levels, and for other employment outcomes. Overall, these findings qualify the role of occupational segregation in sustaining gender disparities in late working life and illustrate the benefits of novel decomposition methods.
[Work in progress]
Details
Abstract: In this article, I propose a novel combination of sequence and age-period-cohort analysis to study retirement entries in Germany. Based on extensive register data from the German Pension Fund, sequence analysis is first used to extract and cluster sub-sequences that end in full retirement, which are then used in an age-period-interaction framework to disentangle the contributions of age, periods and cohorts to the changing prevalence of each cluster. I highlight the benefits of the approach over standard sequence analysis, including a clearer separation of timing and sequencing, larger and more homogenous clusters, and the option to consider time-varying covariates. More generally, the approach presented in the article has many applications in demographic research where age, period and cohort effects may bring about joint but distinct changes in life course transitions.