Life course analysis using sequence analysis

When:13 Mar 2018, 12:30pm - 1:30pm
Venue:Room 221/223, Level 2, John Goodsell Building, UNSW Kensington Campus
Who:Dr Ian Watson, Adjunct Senior Lecturer, SPRC
Dr Ian Watson

Concerns about high levels of young unemployment have been a feature of labour market policy since the 1980s, but over the last decade concern has also focussed on NEETs. These are young people not captured in the unemployment statistics: those Neither in Employment, Education or Training. They have often left the labour market and may be homeless or in poverty. They face an uncertain future, denied the opportunity to build the skills and personal resources needed for a productive working life.

This seminar outlines a research project looking at young people's transition from school to work using the HILDA longitudinal survey data. While it is possible to use annual snap-shots from the data to chart the life course of young people, the HILDA calendar data is much more insightful, as it tracks the activities of respondents on a very detailed basis: 3 times per month over 16 years. Not surprisingly, this level of detail produces a vast amount of data. Fortunately, recent statistical and computing advancing have made it possible to analyse such data in useful ways. In particular, sequence analysis allows researchers to chart the life course of individuals and relate their trajectories to both their demographic and social context. This approach to life course analysis is much more informative than assembling a series of annual snap-shots, which often neglect the links between adjacent labour market states, for example moving back and forth between jobs and unemployment.

Researchers now have access to efficient software routines for sequence analysis in both Stata and R (while SAS and SPSS also provide some features). As well as analysing labour market histories, sequence analysis can be used for other life course trajectories, such as family formation or phased retirement patterns. While calendar data is ideal, the method can be used with any longitudinal dataset where events in a person's life are recorded with reasonable regularity.

Ian Watson has been a freelance researcher for the last 12 years, specialising in labour market research. Prior to that Ian worked at ACCIRT at Sydney University for 13 years. While his current professional work is mostly concerned with the statistical analysis of large datasets, his academic background includes history and sociology. Ian is a visiting research fellow at the Social Policy Research Centre, UNSW and his current academic research deals with wage inequality, unemployment, underemployment and casualisation in the labour market. His most recent book was A Disappearing World: Studies in Class, Gender and Memory and his most recent report was "Family Friendly Working Arrangements: Labour market and workplace trends". More information on his research, including downloads of reports, is available at his website:

Paper: Life course research using sequence analysis: insights into the youth labour market (PDF)