1. Cross-national comparative research with longitudinal data: Understanding youth poverty Maria Iacovou (ISER) with Arnstein Aassve, Maria Davia, Letizia Mencarini, Stefano Mazzucco Funded by JRF as part of the Poverty among Youth: International Lesson for the UK project, under LOOP programme

2. Comparative research at ISER Big EU-funded programmes –EPAG, DYNSOC, ESEC EUROMOD –Tax & benefits microsimulation Lots of stand-alone projects, PhDs, etc. Data –ECHP, EU-SILC, ESS Life chances and living standards (ESRC) –Incomes, work and families, methodology –Combines micro-level analysis and microsimulation –Enlarged EU Youth poverty (JRF funded) –http://www.jrf.org.uk/bookshop/details.asp?pubID=922http://www.jrf.org.uk/bookshop/details.asp?pubID=922

3. Overview of youth poverty programme Descriptive paper –tabulating youth poverty rates across Europe Explaining poverty and poverty transitions –characteristics and events associated with poverty Addressing issues of causality –Does moving out of the parental home “cause” you to be poor, or are young people who are likely to be poor more likely to leave home? Intra-household support –Looking at young people who live with their parents, and classifying them according to who supports whom in the household. Don’t expect much detail. Household/family used interchangeably.

4. Motivation Vulnerability –Unemployment, homelessness, criminality and incarceration, drug abuse, mental health problems, etc etc Lack of research into youth poverty –Lots of research for other vulnerable groups Comparative aspect –Increasing body of knowledge on variations within EU –Do patterns of youth poverty mirror trends among the general population?

5. Data European Community Household Panel Exclude Sweden and Luxembourg (so 13 countries) 8 waves 1994 - 2001 Young people aged 17-35 Computing incomes Use personal income data from year t+1 (which relates to year t) for each individual present in the household in year t If one individual in the household has missing data at year t+1, impute their income at t+1 using income at year t.

6. Welfare regime typology “Social-democratic” –(Scandinavia + Netherlands) “Liberal” –(UK and Ireland) “Corporatist” (Conservative) –France, Germany, Austria, Belgium “Southern” (Residual) –Portugal, Italy, Spain, Greece

7. Poverty, by age: UK

8. Social-democratic regimes

9. “Conservative” regimes

10. Southern regimes Ireland

11. What young people are at greatest risk? 3 age groups: 16-19, 20-24, 25-29 Poverty risk reduces with age, and is increased on leaving home

12. Leaving home and poverty A bit of a puzzle

13. Multivariate analysis Cross-sectional – who is poor (and deprived) –Pooled sample across waves –Controls: age, sex, employment/unemployment/studying, living arrangements, marital status, number of children Entry into & exit from poverty (and deprivation) –Pairs of individuals present in sample in t and t+1 –Longitudinal – who becomes poor (or deprived) –Also control for events: moving out of the parental home, having a baby, etc. In all cases –Probit regressions for poverty, linear models for deprivation –Control for multiple observations –Marginal effects reported

14. Results from multivariate analysis

15. More results Moving swiftly onwards Deprivation

17. More on deprivation

18. Poverty entry

19. More on poverty entry

20. Exits from poverty

21. More on poverty exits

22. Does leaving home “cause” poverty? Or is it a selection effect? –do we just observe higher levels of poverty among those who have left home, because those at higher risk of poverty are more likely to leave home at younger ages? Possibly a bit of both?

23. Propensity score matching We want to compare risk of poverty in two situations –Remaining in the parental home, and living independently For obvious reasons, we can’t do this for individuals –No “counterfactual” –“Match” individuals who are identical in all observable characteristics, except living arrangements Not without problems –Some people can’t be matched –Oldest Scandinavians; youngest Southern Europeans –“Common support” problem Importance of longitudinal data

24. PSM procedure Identify “treatment” and “control” groups –those who did and did not leave home For both groups: synthesise counterfactuals –We use up to three “near neighbours” Average treatment effect on the treated (ATT) –Start with treatment group and synthesise counterfactuals –ATT = poverty rate in treatment gp less rate in control gp –For those who did leave home: The extra risk of entering poverty arising from leaving home. Average treatment effect on the control (ATC) –For those who did not leave home: The extra risk of entering poverty which would have arisen if they had left home

25. ATT estimates

26. –Significant selection effects –Young people who are most likely to experience poverty if they leave home …… are actually more likely to remain at home. –Analysis ignoring this underestimates effect of leaving home.

27. Effects on treatment and control Rational in so far as those who are at higher risk of poverty are more likely to remain at home – except in Finland and Denmark. But we haven’t uncovered a “rational” reason for the huge differences between countries.

28. Conclusions Young people are at generally high risk of poverty Leaving home is the most important trigger Having children and being unemployed are also risk factors

29. Policy conclusions Child poverty measures –also reduce poverty among young adults still living at home. Financial assistance –in first year or two of living away from the parental home. Scandinavian systems of support for young parents –family support plus family-friendly labour markets. Austrian and German style paid apprenticeships –effective in keeping youth poverty rates extremely low. Employment plays a part in reducing youth poverty –but getting a job is not enough; keeping a job is important too.

30. the end

31. Including Ireland… back