Research Group

Labor Demography

At a Glance Projects Publications Team

Project

Modeling and Measuring Working Life

Conducted by Christian Dudel, Mikko Myrskylä; Daniel Christoph Schneider, Carla Rowold

Detailed Description

Understanding how life course trajectories unfold is a complex task. Multistate models provide a useful toolbox to handle this complexity and to gain a better understanding of life course dynamics. In this project, we assess existing methods and develop innovative extensions and new approaches, with a focus on discrete time multistate models, which we use to study work and health trajectories. The statistical properties of these models are studied mathematically and through simulations. The specific topics we explore include variance estimation, the application of Markov chains with rewards, and causal inference in multistate models. We also provide code and open-source software that implements our methods. Moreover, we assess how the measurements and definitions used affect results about working lives. For instance, in the literature, a wide range of definitions of retirement are employed, making it difficult to compare the findings of different studies. Our assessment will provide guidance on the extent to which these results are driven by such differences.

Publications

Moretti, M.; Riffe, T.; Lorenti, A.:

Multistate analysis and decomposition of disability-free life expectancy trends at mid-to-older ages in Italy, 2004–19

Population Studies 80:1, 123–138. (2026)    

Sulak, H.; Dudel, C.; Loichinger, E.; Klüsener, S.:

Trends in working life expectancy and untapped employment potential in an ageing population: the case of Germany

European Journal of Population 42, 1–24. (2026)    

Rowold, C.:

Housewives never retire!? Gender biases in popular sample definitions for studies on the elderly

MPIDR Working Paper WP-2024-025. (2024)    

Schneider, D. C.; Myrskylä, M.; van Raalte, A. A.:

Flexible transition timing in discrete-time multistate life tables using Markov chains with rewards

Population Studies 78:3, 413–427. (2024)    

Schneider, D. C.:

Statistical inference for discrete-time multistate models: asymptotic covariance matrices, partial age ranges, and group contrasts

MPIDR Working Paper WP-2023-041. (2023)    

Schneider, D. C.; Myrskylä, M.:

Statistical inference for discrete-time multistate models: extensions to Markov Chains with rewards

MPIDR Working Paper WP-2023-042. (2023)    

Dudel, C.; Schneider, D. C.:

How bad could it be? Worst-case bounds on bias in multistate models due to unobserved transitions

Sociological Methods and Research 52:4, 1816–1837. (2021)    

Schneider, D. C.; Myrskylä, M.; van Raalte, A. A.:

Flexible transition timing in discrete-time multistate life tables using Markov chains with rewards

MPIDR Working Paper WP-2021-002. (2021)    

Dudel, C.; Myrskylä, M.:

Estimating the number and length of episodes in disability using a Markov chain approach

Population Health Metrics 18:15, 1–9. (2020)    

Dudel, C.:

Expanding the Markov chain tool box: distributions of occupation times and waiting times

Sociological Methods and Research, 1–28. (2018)    

Dudel, C.:

Expanding the Markov chain tool box: distributions of occupation times and waiting times

MPIDR Working Paper WP-2017-017. (2017)