Centre for Market and Public Organisation Parental income and child outcomes Paul Gregg, Carol Propper and Elizabeth Washbrook Avon Local Group of the Royal Statistical Society Meeting Bristol, 26 th May 2009

Family income and child outcomes Drivers of the developmental deficits of low income children are of interest to academics and policymakers Lifecycle models of human capital formation (Cunha and Heckman 2007; Carneiro and Heckman 2003) Labour reforms to tax credits, child care and early education, child benefit, maternity leave (e.g. Brewer 2007) Developmental outcomes in childhood are related to multiple aspects of adult well-being and have long term social consequences Cognitive and educational outcomes – IQ, test scores, grades Non-cognitive and socio-emotional outcomes – behavior, self-concept, motivation, attention Physical health – obesity and poor nutrition, asthma, injuries, illnesses Adult outcomes associated with at least one class of child outcome Years of schooling, qualifications, employment and earnings, mental health, life expectancy and morbidity, antisocial and risky behavior, crime, fertility

This paper Uses data from an unusually rich birth cohort dataset to compare the income gradients in six developmental outcomes in mid-childhood The income gradient is the unconditional association between income and the outcome – one broad measure of social inequality Develops a descriptive decomposition method to give an overview of the underlying associations that give rise to the observed income gradients We estimate the portion of the observed gradients predicted by income- related differences in a wide range of potential explicators Estimates can be interpreted in the light of two approaches taken in the literature Reduced form OLS studies of the relationship between child poverty and outcomes (e.g. Duncan and Brooks-Gunn 1997). [Precursors to studies on the causal effects of income, e.g. Blau 1999; Dahl and Lockner 2005.] Correlational SEM studies of the mediators between family income and child outcomes (Guo and Harris 2000; Yeung et al., 2002)

The contribution of descriptive estimates The associations identified in our estimates are not causal. They do not, for example, adjust for reverse causation or the influence of unobservable third factors such as inherited ability Causal approaches provide crucial evidence on parts of the puzzle of why low income children fall behind The effect of increasing cash benefits for low income families The effects specific factors on outcomes (intervention programmes, smoking, birth order, inherited characteristics) But causal studies relying on specific mechanisms cannot give an overview of relative importance of all the potential factors that drive the intergenerational persistence of poverty. Single and teen parenthood, low parental education, worklessness and deprived neighborhoods – control variables Parental stress and depression, social connections, child care experiences, unhealthy environments, parenting behaviors - mediators

Contribution of our paper What are the upper bounds on the effects of interventions targeted to specific factors in terms of reducing social inequality in child well-being? Conclusions depend on whether we focus on a single or multiple classes of outcomes. Some factors are associated with all three types of outcome. Examples are breast feeding and child nutrition, discipline and maternal locus of control. Some factors are strongly associated with some outcomes but not others. Maternal social networks and parental smoking explain the non-cognitive and health gradients but not the cognitive. Some factors have opposing associations with certain aspects of development. Lack of car ownership and poor housing are associated with lower risk of obesity; lower attendance at center-based child care is associated with fewer behavioral problems

Data: The ALSPAC cohort 9476 children born in Bristol and the surrounding regions (Avon) in 1991/2 Population 1 million, mixture of rural, suburban and urban, broadly nationally representative Census of pregnant mothers rather than random sample, very high frequency Mother-completed postal questionnaires Teacher-completed postal questionnaires Hands-on assessments in clinics at ages 7, 8 and 9 Matched to Key Stage national school test results from the National Pupil Database

Outcome measures Cognitive IQ at age 8. WISC-III UK. Academic achievement at age 7. Key Stage 1 national school tests (KS1). Reading, writing and mathematics. Non-cognitive Locus of control at age 8. Child completed. Short form of Nowicki- Strickland Internal-External scale for children. Self esteem at age 8. Child completed. Short form of Harters Self Perception Profile for Children. Behavioral problems at age 7. Teacher rated. Strengths and Difficulties Questionnaire. Hyperactivity, peer relations, conduct problems, emotional problems. Health Fat mass at age 9. Total body dual energy X-ray absorptiometry (DXA scans).

The income gradient O ij = δY i + e ij e ij Y i Income (Y) Child Outcome j (O j ) δ O ij is the jth outcome of the ith child Y i is the log of average disposable equivalised household income at child age 3 and 4 in 1995 prices e ij is an orthogonal error term All outcomes standardised to mean 100, SD 10. In presentation, the signs of the coefficients are adjusted, such that positive coefficients represent more beneficial outcomes in all cases.

Income gradients in outcomes in middle childhood Equivalised disposable weekly household income age 3/4 (1995 GBP) Score (mean 100, SD 10) IQ (5.85) KS1 (5.46) Locus of control (3.30) Self esteem (1.71) Behavior (2.01) Fat mass (1.34) p < 0.01 for all gradients

Control variables Household demographics: Single parenthood, siblings, mothers age Labor market status: Mothers and partners employment and occupational class Education: Mothers, partners and maternal grandparents qualifications Neighborhood: Local deprivation (IMD for ward at birth), social housing Mediating variables Maternal psychosocial functioning: Anxiety/depression, weighted life events, financial difficulties, parental relationship, frequency of smacking, social networks, locus of control Preschool childcare: Type and intensity, between birth and age 3, between age 3 and school entry Health & health behaviours: Birth weight and gestation, parental smoking, breastfeeding, diet at age 3 Home learning environment: Books and toys, maternal teaching, educational outings, mothers and partners reading and singing with child Physical home environment: Car ownership, garden, noise, crowding, damp/mould School quality and mix: Fixed effects

Decomposing the income gradient O ij = δY i + e ij e ij Y i Income (Y) Child Outcome j (O j ) δ

Modelling framework Income (Y) Controls (C) e.g. parental education, family structure Mediators (M) e.g. home learning environment, diet Child Outcome j (O j ) γjγj θjθj πjπj β λ α (1)O ij = γ j M i + θ j C i + π j Y i + μ ij μ ij M i, C i, Y i

Modelling framework Income (Y) Controls (C) e.g. parental education, family structure Mediators (M) e.g. home learning environment, diet Child Outcome j (O j ) γjγj θjθj πjπj β λ α (2)M i = βC i + λY i + η i η i C i, Y i

Modelling framework Income (Y) Controls (C) e.g. parental education, family structure Mediators (M) e.g. home learning environment, diet Child Outcome j (O j ) γjγj θjθj πjπj β λ α (3)C i = αY i + ν i ν i Y i

Modelling framework Income (Y) Controls (C) e.g. parental education, family structure Mediators (M) e.g. home learning environment, diet Child Outcome j (O j ) γjγj θjθj πjπj β λ α (4)O ij = (γ j βα + γ j λ + θ j α + π j ) Y i + error = δ j Y i + e ij

Modelling framework δ j = γ j βα + γ j λ + θ j α + π j The unconditional income gradient can be written as the sum of a set of path coefficients. Each path coefficient is the product of the partial effects of one variable on another. If any link in the chain is zero, the path coefficient will be zero. Path coefficients calculated by multiplying and summing the OLS coefficients from the underlying regressions. Standard errors estimated by bootstrapping with 200 repetitions. Path coefficients can be combined in different ways to give alternative decompositions of the income gradient.

Income gradient decompositions - summary

Income gradient decompositions – disaggregated mediators

Income gradient decompositions – mediators of income and controls

Conclusions Our accounting exercise produces a number of findings in line with previous research Income gradients are steeper for cognitive outcomes than for non- cognitive or health outcomes The estimated effect of income drops steeply when other forms of socio- economic disadvantage are controlled Mediators between income and outcomes are many and diffuse Less cognitive stimulation in the home helps to account for the cognitive deficits of low income children; poorer maternal psychosocial functioning helps to account for their behavioral deficits

Conclusions Our comparative approach provides new insights that may be missed in more narrowly-focused studies Maternal psychosocial functioning and health-related behaviors appear as important as the home learning environment in accounting for the cognitive deficits of low income children Some factors have a modest role to play in explaining multiple gradients (e.g. breast feeding, discipline) A focus only on cognitive outcomes may miss the adverse consequences of certain factors for other dimensions of development (e.g. smoking, social networks) Not everything that high income parents do is necessarily good for their children. Behaviors that promote cognitive development (learning- focused environments) could have adverse consequences for physical health