New research method helps better analyse lifecourse

Equal Lives research uses new method to see whether employment life-courses converge after reunification in the former East Germany and West Germany.

Assessing and analysing the different paths that groups of people follow in life and comparing these in a meaningful way has been a challenge for researchers. But a new method, developed as part of the Equal Lives project, is helping researchers make inroads in this area.

In a new paper published in the Sociological Methodology journal, Equal Lives PI Anette Fasang from Humboldt University in Berlin and Tim Liao from the University of Illinois present the new method and then use it to analyse whether the working lives of people living in the former East and West Germany became more similar after reunification following the fall of the Berlin Wall in 1989.

Using data from the German National Education Panel Study, the researchers adapted 2 established research tools, the Bayesian information criterion (BIC) and the likelihood-ratio test (LRT) to assess the degrees of difference between groups of individuals in more detail.

The researchers say that unlike previous methods, the adaption provides a useful measure for degrees of difference, that is, the substantive significance, and the statistical significance of differences between predefined groups of life-course trajectories.

Using the method in this way showed that convergence of employment life-courses around German reunification was stronger for men than for women and that it was most pronounced in terms of the duration of employment states but weaker for their order and timing in the life-course.

The researchers say that convergence of women’s employment lives set in earlier, the researchers say, reflecting the move toward a more gender-egalitarian division of labour in West Germany unrelated to reunification.

Commenting on the research, Anette said:

This method helps us address a long-standing inadequacy of social sequence analysis. Our work here clearly demonstrates its usefulness of for assessing group differences in sequence data.