1. Poverty trajectories after risky life events in Germany, Spain, Denmark and the United Kingdom: a latent class approach Leen Vandecasteele Post-doctoral research fellow Cathie Marsh Centre for Census and Survey Research University of Manchester

2. Research aims 1. Exploration of poverty trajectories after experiencing a risky life event like partnership dissolution, job loss and leaving the parental home 2. Investigate typologies in terms of social class, education level and gender of household head

3. Life events and poverty Biographisation of poverty: poverty is associated with particular events and stages in the lifecourse, breaks in the standard life course Study of life course events leading to poverty Demographic events: partnership dissolution, birth of child, leaving the parental home, widowhood Labour related events: retirement, transition to unemployment In this study focus on three events: partnership dissolution, job loss, leaving parental home

4. Poverty entry risk after risky life events: mainly studied through the regression approach Less suitable for gaining insight in longer-term effects of life events Latent class analysis: a more complete picture of the poverty dynamics triggered by risky life events Longer term picture differentiation according to the duration of poverty Alternative poverty patterns Research motivation

5. Latent class analysis Allows to determine latent clusters in the data on the basis of categorical indicators Aim: descriptive method to explore poverty patterns after life events Possibilities Likelihood of poverty entry after risky life event Length of poverty spell after life event Find undetected patterns overlooked by regression analysis

6. Research method European Community Household Panel Socio-economic panel study: 1994-2001 Countries under study: Denmark, Germany, Spain, United Kingdom 13 country pooled dataset for poverty entry after partnership dissolution Latent class analysis: Latent classes are unobservable (latent) subgroups or segments Technique initially developed for categorical data Probability based classification ~ loglinear models Based on vectors with observed outcomes Hypothesize a number of latent classes Estimate conditional probability of obtaining the observed vector given membership of a given class ! Outcome is a categorical latent variable

7. Results Four main latent classes can be discerned Persistent non-poor Group with transient poverty risk Group with longer-term poverty risk Late poverty entrants Not all poverty patterns have the same prevalence in all countries under study

8. Conditional probabilities of being poor in the years after partnership dissolution

9. Poverty trajectories after partnership dissolution: latent class sizes Transient poverty risk is more likely than longer-term or late poverty entry risk after partnership dissolution

10. Conditional probability of being poor in the years after job loss

11. Poverty trajectories after job loss Separate country analyses suggest same latent classes in the four countries Multigroup latent class analysis Check whether the latent classes are statistically equivalent in the four countries Model allows for partial measurement invariance

12. Poverty trajectories after job loss: country differences Latent class sizes of poverty patterns after job loss

13. Further insight into the different poverty trajectories Inclusion of covariates in latent class solutions Inactive covariates: do not influence latent class solution Relative risk ratios given

16. Additional on Research method Selected population All persons experiencing partnership dissolution, job loss or leaving the parental home within the time frame of the ECHP panel Job loss = unemployment entry Not poor in year before risky life event Time frame Poverty trajectories during five years after risky life event Right censoring? A complete 5-year follow-up was not achieved for all Attrition, end of panel study Missing values and censored cases included in the Likelihood estimation Missing values can be considered MAR (Missing At Random)

17. Research method Poverty Income poverty < 60% of the equivalized median household income in a given year and country Poverty measure time lag adjusted Household income measure in ECHP dataset refers to previous calendar year Recalculation of current year household income - matched to current year household composition Weighted Nonresponse Design effects - country population size