Income-related gaps in school readiness in the U.S. and the U.K.

1. Income-related gaps in school readiness in the U.S. and the U.K. Jane Waldfogel Elizabeth Washbrook Child Well-Being and Social Investments in the U.S. and Other Countries APPAM Fall Conference Washington D.C., 5-7 November, 2009 Research funded by Russell Sage Foundation, Sutton Trust, Leverhulme Trust, ESRC and NICHD

2. Motivation • A growing inter-disciplinary literature suggest the early years are a “critical” period in the development of human capabilities • Socio-economic differences in school readiness may cast a long shadow • The returns to policy interventions in the early years may be greater than at later ages • The U.S. and the U.K. are both characterized by high income inequality and low intergenerational mobility, but have very different public policy environments around young families (e.g. paid maternity leave, universal child benefit, universal free nursery places for 3- and 4-year olds)

3. Aims • Document the gaps in indicators of school readiness between children born into different income quintile groups at the start of the 21st century, contrasting • United States and United Kingdom • Cognitive and behavioral outcomes • Explore how far the gaps can be explained by conditioning on • A sparse set of comparable demographic characteristics - race/ ethnicity, maternal education, age, family structure and size • A rich set of “policy-relevant” mechanisms – parenting behaviors, neighborhood and material circumstances, family health and well-being, pre-school care arrangements

4. Some caveats • No assumption that income “causes” differences in parenting behavior and lifestyle factors • Our interest is in the areas in which policy interventions may potentially be effective in closing the income-related gaps – regardless of whether they are caused by income or something else • Many other factors differ across the two countries besides policy – we cannot know the counterfactual (at least in a cross-sectional study)

5. Data • Two nationally representative birth cohort studies of children born at the start of the 21st century • US: ECLS-B data on 10,000 children born in 2001 • UK: MCS data on 19,000 children born in 2000/1 • Sample selection criteria • Biological mother main respondent at all 3 waves – 9 months, 2 (3) years and 4 (5) years in the US (UK) • Valid scores for all cognitive and behavioral assessments • Working samples 7250 (US) and 8864 (UK) • Income quintile groups • Defined [using survey weights] according to gross, real, equivalized household income averaged over the three waves.

6. Outcome scores • Four composite indices constructed from multiple scales using principal components analysis. Normed to mean zero, standard deviation one. • US Cognitive. 6 sub-scales (all age 4). Receptive vocabulary; Expressive language; Mathematics; Literacy; Color knowledge; Copying ability • UK Cognitive. 5 sub-scales. Bracken School Readiness Assessment (age 3); British Ability Scales Naming Vocabulary (ages 3 and 5); Pattern Construction (age 5) and Picture Similarities (age 5) • US Behavior. 21 mother-report items (all age 4). Most taken from the PKBS-2. • UK Behavior. 25 mother-report items (all age 5). From the SDQ scales on hyperactivity/inattention; conduct problems; emotional symptoms; peer problems; pro-social behavior

7. Figure 1. Mean gross equivalized annual household income, by income quintile group Reference group

8. Figure 2. Cognitive outcome gaps, with no additional controls

9. Figure 3. Behavior outcome gaps, with no additional controls

10. Figure 4. Cognitive outcome gaps, with additional sets of controls

11. Figure 5. Behavior outcome gaps, with additional sets of controls

12. Figure 6. Breakdown of the US cognitive gaps

13. Figure 7. Breakdown of the UK cognitive gaps

14. Figure 8. Breakdown of the US behavior gaps

15. Figure 9. Breakdown of the UK behavior gaps

16. Conclusions I • Income-related gaps in school-readiness are broadly similar in the US and the UK… • Large gaps in cognitive outcomes (more than a standard deviation gap between richest and poorest quintiles) • Smaller gaps in behavioral outcomes • Almost entirely explained by observable factors • …with some second-order differences • The U.S. shows greater inequality in cognitive outcomes at the top of the income distribution • But less inequality in behavior outcomes at the bottom of the income distribution

17. Conclusions II • Material circumstances matter more for cognitive development, family health and well-being for behavioral development • Parenting behavior matters a lot for both. But can it be changed by policy? • There is evidence that it can (e.g. NFP), in which case policy may have a double pay-off • But parenting programs are potentially expensive, politically difficult and have uncertain returns

18. Conclusions III • Differences in preschool care arrangements do not explain the outcome gaps between low- and higher-income children in either country. • We do not measure quality or consistency of arrangements • Programs are already targeted (Head Start and Sure Start) • We assume the effects of child care are the same for all • Early education programs may still be the most effective policy mechanism to compensate for income-related differences in parenting and the home environment

19. Next steps • We aim to contrast these findings with those from two “high inequality, high intergenerational mobility” countries – Canada and Australia (with Miles Corak and Bruce Bradbury) • Does Canadian and Australian success in promoting equality of life chances begin in the early years? Or does it come later in school or the labor market? • How does the picture change if we compare children across countries with the same amounts of money income (rather than relative position)?