Crossnational comparative research with longitudinal data: Understanding youth poverty

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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

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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?pubID9
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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.
• Dont expect much detail. Household/family used
  interchangeably.

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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?

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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 t1 (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 t1, impute their income at t1
  using income at year t.

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Welfare regime typology

• Social-democratic
• (Scandinavia Netherlands)
• Liberal
• (UK and Ireland)
• Corporatist (Conservative)
• France, Germany, Austria, Belgium
• Southern (Residual)
• Portugal, Italy, Spain, Greece

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Poverty, by age UK
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Social-democratic regimes
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Conservative regimes
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Southern regimes
Ireland
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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

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Leaving home and poverty
A bit of a puzzle
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Multivariate analysis

• Cross-sectional who is poor (and deprived)
• Pooled sample across waves
• Controls age, sex, employment/unemployment/studyi
  ng, living arrangements, marital status, number
  of children
• Entry into exit from poverty (and deprivation)
• Pairs of individuals present in sample in t and
  t1
• 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

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Results from multivariate analysis
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More results

• Moving swiftly onwards
• Deprivation

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Deprivation
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More on deprivation
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Poverty entry
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More on poverty entry
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Exits from poverty
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More on poverty exits
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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?

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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 cant do this for
  individuals
• No counterfactual
• Match individuals who are identical in all
  observable characteristics, except living
  arrangements
• Not without problems
• Some people cant be matched
• Oldest Scandinavians youngest Southern Europeans
• Common support problem
• Importance of longitudinal data

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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

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ATT estimates
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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.

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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 havent uncovered a rational reason for
  the huge differences between countries.

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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

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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.

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the end
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Including Ireland
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