Cross-national comparative research with longitudinal data: Understanding youth poverty

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=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

16. Poverty entry

17. More on poverty entry

18. Exits from poverty

19. More on poverty exits

20. 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?

21. 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

22. 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

23. ATT estimates

24. ATT estimates • 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.

25. 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.

26. 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

27. 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.

28. the end

29. Including Ireland… back