Income-Related Disparities in School Readiness: A Comparative Study of the U.S. and the U.K.

 
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
 
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)
 
Aims
 
Document the gaps in indicators of school readiness
between children born into different income quintile
groups at the start of the 21
st
 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
 
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)
 
Data
 
Two nationally representative birth cohort studies of children
born at the start of the 21
st
 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.
 
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
 
 
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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
 
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
 
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
 
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)?
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Documenting income-related gaps in school readiness between children born into different income quintiles in the U.S. and the U.K., this research explores cognitive and behavioral outcomes and examines how demographic characteristics and policy-relevant mechanisms may influence these disparities. The study uses nationally representative birth cohort data from the early 21st century to analyze the impact of income on school readiness, highlighting potential areas for policy interventions.


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  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 21stcentury, 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 21stcentury 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 80 USA UK 70 60 Thousands US dollars Reference group 50 40 30 20 10 0 Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5 Income quintile group 95% CI

  8. Figure 2. Cognitive outcome gaps, with no additional controls = IncQ 1 + + + + ' + Outcome IncQ IncQ IncQ X 1 2 2 4 4 5 5 i i i Q1 coef Q2 coef Q4 coef Q5 coef 1.00 0.80 0.60 Standard deviations 0.40 0.20 0.00 . -0.20 -0.40 -0.60 -0.80 US UK -1.00

  9. Figure 3. Behavior outcome gaps, with no additional controls = IncQ 1 + + + + ' + Outcome IncQ IncQ IncQ X 1 2 2 4 4 5 5 i i i Q1 coef Q2 coef Q4 coef Q5 coef 1.00 0.80 0.60 Standard deviations 0.40 0.20 0.00 . -0.20 -0.40 -0.60 -0.80 US UK -1.00

  10. Figure 4. Cognitive outcome gaps, with additional sets of controls = IncQ 1 + + + + ' + Outcome IncQ IncQ IncQ X 1 2 2 4 4 5 5 i i i Q1 coef Q2 coef Q4 coef Q5 coef No controls Demographic controls 1.00 All controls 0.80 0.60 Standard deviations 0.40 0.20 0.00 . -0.20 -0.40 -0.60 -0.80 US UK US UK US UK -1.00

  11. Figure 5. Behavior outcome gaps, with additional sets of controls = IncQ 1 + + + + ' + Outcome IncQ IncQ IncQ X 1 2 2 4 4 5 5 i i i Q1 coef Q2 coef Q4 coef Q5 coef No controls Demographic controls 1.00 All controls 0.80 0.60 Standard deviations 0.40 0.20 0.00 . -0.20 -0.40 -0.60 -0.80 US UK US UK US UK -1.00

  12. Figure 6. Breakdown of the US cognitive gaps USA Income coeffcient Maternal education # Children Nationality & ethnicity Family structure Mother's age Parenting Neighborhood & mat circs Family health and well-being Care arrangements -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 Standard deviations Low income penalty High income advantage

  13. Figure 7. Breakdown of the UK cognitive gaps UK Income coeffcient Maternal education # Children Nationality & ethnicity Family structure Mother's age Parenting Neighborhood & mat circs Family health and well-being Care arrangements -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 Standard deviations Low income penalty High income advantage

  14. Figure 8. Breakdown of the US behavior gaps USA Income coeffcient Maternal education # Children Nationality & ethnicity Family structure Mother's age Parenting Neighborhood & mat circs Family health and well-being Care arrangements -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 Standard deviations Low income penalty High income advantage

  15. Figure 9. Breakdown of the UK behavior gaps UK Income coeffcient Maternal education # Children Nationality & ethnicity Family structure Mother's age Parenting Neighborhood & mat circs Family health and well-being Care arrangements -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 Standard deviations Low income penalty High income advantage

  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, highintergenerational 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)?

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