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Since the 1970s people have retired increasingly early across advanced societies. Parallel to this trend, numerous institutional early retirement pathways evolved, such as bridge unemployment and pre-retirement schemes. This article compares retirement in Britain and Germany to show how individuals progress through these institutional retirement pathways. The analysis uses longitudinal data and recent innovations in sequence analysis to capture the sequential nature of retirement as a series of transitions over time. As expected, prominent institutional retirement pathways are mirrored in individual retirement trajectories. Beyond these expected patterns, there are pronounced regularities in individual retirement trajectories outside of explicit institutional pathways. The 'institution of the family' is an additional powerful force in structuring women's retirement. Access to advantageous institutional retirement pathways is stratified by gender, education, income, and health. The article concludes that specific population groups, particularly women, are systematically excluded from protective institutional early retirement pathways in Britain and Germany.

In this article the authors draw attention to the most recent and promising developments of sequence analysis. Taking methodological developments in life course sociology as the starting point, the authors detail the complementary strength in sequence analysis in this field. They argue that recent advantages of sequence analysis were developed in response to criticism of the original work, particularly optimal matching analysis. This debate arose over the past two decades and culminated in the 2000 exchange in Sociological Methods & Research. The debate triggered a "second wave" of sequence techniques that led to new technical implementations of old ideas in sequence analysis. The authors bring these new technical approaches together, demonstrate selected advances with synthetic example data, and show how they conceptually contribute to life course research. This article demonstrates that in less than a decade, the field has made much progress toward fulfilling the prediction that Andrew Abbott made in 2000, that "anybody who believes that pattern search techniques are not going to be basic to social sciences over the next 25 years is going to be very much surprised" (p. 75).

The relationship between processes and time-varying covariates is of central theoretical interest in addressing many social science research questions. On the one hand, event history analysis (EHA) has been the chosen method to study these kinds of relationships when the outcomes can be meaningfully specified as simple instantaneous events or transitions. On the other hand, sequence analysis (SA) has made increasing inroads into the social sciences to analyze trajectories as holistic "process outcomes." We propose an original combination of these two approaches called the sequence analysis multistate model (SAMM) procedure. The SAMM procedure allows the study of the relationship between time-varying covariates and trajectories of categorical states specified as process outcomes that unfold over time. The SAMM is a stepwise procedure: (1) SA-related methods are used to identify ideal-typical patterns of changes within trajectories obtained by considering the sequence of states over a predefined time span; (2) multistate event history models are estimated to study the probability of transitioning from a specific state to such ideal-typical patterns. The added value of the SAMM procedure is illustrated through an example from life-course sociology on how (1) time-varying family status is associated with women's employment trajectories in East and West Germany and (2) how German reunification affected these trajectories in the two subsocieties.