We analyze the role that early years policy might play in narrowing educational attainment gaps. We begin by examining gaps in school readiness between low-, middle-, and high-income children, drawing on data from new large and nationally representative birth cohort studies in the USA and UK. We find that sizable income-related gaps in school readiness are present in both countries before children enter school and then decompose these gaps to identify the factors that account for the poorer scores of low-income children. We then consider what role early years policy could play in tackling these gaps, drawing on the best available evidence to identify promising programs.
One of the key challenges in addressing inequality of educational attainment, and promoting social mobility, is that substantial gaps in school readiness are already present at school entry [
Interest in the early years has also been spurred by new research and scholarship in fields such as neuroscience, developmental psychology, and economics [
At the same time, however, there are clearly some limits to what early years programs can accomplish. Some portion of the differences that emerge in the early years will be due to factors that are not readily altered by policy. A further challenge is that not all early years programs are equally effective, high-quality programs are not inexpensive, and even the most promising model programs may not work as well when delivered on a large scale. There are also thorny issues regarding the extent to which such programs are best delivered universally or targeted to disadvantaged groups.
In this paper, we use two types of evidence to analyze the role that early years policy might play in narrowing educational attainment gaps. We begin by documenting how large the gaps in school readiness are between low
What role could early years policy play in tackling these types of differences? We consider this question in the second part of the paper. Drawing on the best available evidence—emphasizing results from random assignment studies where available—we discuss what policy reforms would be most effective in helping to close early gaps. To play a role in closing early gaps, policies must (1) effectively address a factor that is consequential for early gaps and (2) do more to improve the school readiness of disadvantaged children than more advantaged children. We identify a number of promising programs that have the potential to meet these criteria.
We use data from two nationally representative birth cohort studies to document the magnitude of the income-related gaps in school readiness in the USA and UK. For the USA, we use data from the Early Childhood Longitudinal Study-Birth Cohort (ECLS-B), which gathered data on over 10,000 children born in 2001, with interviews at roughly 9 months, 2 years, and 4 years after birth. For the UK, we use data from the Millennium Cohort Study (MCS), which collected data on over 19,000 children born in 2000 and 2001, with interviews at 9 months, 3 years, and 5 years after birth. Both surveys oversampled some populations of interest, but when properly weighted, the data are nationally representative of all families with newborns. Not all children remain in the sample for all waves, and in addition, some children have missing data on cognitive or behavior outcomes. (Cases that are missing cognitive or behavior outcome data differ somewhat from the cases that have complete outcome data; in particular, they are disproportionately likely to be from racial/ethnic minority groups or immigrant groups.) For the USA, we are able to use a total of 8,900 children in constructing our income groups and 7,950 in analyzing cognitive and behavior outcomes. (All reported ECLS-B frequencies are rounded to the nearest 50 in accordance with NCES requirements.) For the UK, we use a total of 13,423 children in constructing our income groups and 10,476 in our analysis of cognitive and behavior outcomes.
We begin by dividing our samples into groups defined by family income over the course of early childhood (i.e., averaged over the three survey waves). Specifically, we divide families into income quintiles, with the first quintile defined as the families with incomes in the bottom fifth of the income distribution for all families with newborns, and so on. Descriptive statistics (not shown but available on request) indicate that the bottom quintile in each country has family incomes that place them below the country’s official poverty line. In contrast, the typical family in the top quintile has income more than 4 times the poverty line in the UK and more than 6 times in the USA. (The USA uses an absolute poverty line, while the UK tends to use a relative threshold. Here we use an absolute line for the UK (60% of median equivalized disposable income in 1996/1997, uprated for inflation) to facilitate comparison.)
How much does school readiness vary across these income groups? Figure
Mean school readiness scores in the ECLS-B (US) cohort at age 4, by income quintile (
Figure
Mean school readiness scores in the MCS (UK) cohort at ages 3 and 5, by income quintile (
However, income-related differences in behavior problems are more pronounced in the UK than in the USA. This finding seems to be mainly driven by the higher behavior problem scores of the bottom income quintile in the UK. We can only speculate as to the reasons for this. Given that the UK measure comes from age 5 when many of the children have already started school (as compared to the US measure from age 4), the higher levels in the UK may reflect the emergence of larger gradients with age or adjustment difficulties low-income children have on starting school.
To identify the factors that account for the income-related gaps in school readiness, we take advantage of the very detailed data in the US study, including direct observations of parenting style as well as measures of the home environment, maternal health and health behaviors, child health, and early childhood care and education, as well as family income and demographics. (We focus on the US data alone in this section in order to avoid the myriad data comparability issues between the two datasets. In unpublished work we find that the major explanatory factors are quite similar across the two countries.) We focus on the three cognitive outcomes because the income-related gaps in the behavioral outcomes tended to be small, but note that behavior is an equally important dimension of school readiness and one that is targeted by many of the programs discussed in the following sections. We use a two-step method to decompose the income-related gaps into the share accounted for by each of these major domains. In the first step, we use a simple unconditional regression model to estimate how much each of the contributing variables varies by income quintile. In the second step, we estimate the return (or penalty) associated with each variable by regressing the cognitive score on all the contributing factors, including demographic variables such as race/ethnicity, family size and structure and maternal age, and the income quintile dummies simultaneously. (Results of the two steps of the analysis are not shown here but are included in a more detailed companion paper [
To illustrate, approximately 12% of children in the lowest income quintile attended prekindergarten, compared with 15% in the middle income quintile. Children who attended pre-K scored, on average, 6.8 percentile points higher on the ECLS-B literacy assessment than children who did not attend, all else equal. The portion of the income gap in literacy between quintiles 1 and 3 attributed to differential pre-K attendance is then
It is important to note that the factors we examine may be markers, rather than causes of, child outcomes. For example, children with more educated mothers may score higher on cognitive tests, but this does not tell us that increasing maternal education would necessarily cause improved test scores for children. Maternal education (and the other factors we consider) may at least in part be operating as markers for other unmeasured attributes that differ across families and that are themselves causally related to child outcomes. It is important to keep this caveat in mind when interpreting our results. Nevertheless, the choice of many of our predictor variables such as parenting behaviors and birth weight are informed by the results of the program evaluations discussed below, which provide strong evidence that at least part of the correlation with child outcomes represents a causal effect.
Figures
Decomposition of the income-related gaps in cognitive school readiness scores in the ECLS-B (US) cohort.
Percentile point gap in mean scores between incomequintile | ||||||
Domain | Literacy score | Mathematics score | Language score | |||
Parenting style | −2.67 | 2.87 | −2.98 | 2.96 | −4.38 | 4.15 |
Home learning environment | −2.62 | 3.05 | −2.52 | 2.78 | −2.24 | 3.10 |
Maternal health & health behaviours | −0.65 | 0.99 | −1.00 | 1.28 | −0.47 | 0.52 |
Child health | −0.48 | 0.48 | −0.59 | 0.42 | −0.53 | 0.54 |
Child care (excluding Head Start) | −0.72 | 1.16 | −0.64 | 1.44 | −0.60 | 0.94 |
Ever in Head Start | 1.17 | −0.62 | 0.85 | −0.45 | 0.97 | −0.51 |
Mother's education | −1.85 | 4.00 | −1.62 | 3.34 | −0.29 | 1.41 |
Demographics | −2.36 | 2.22 | −2.47 | 2.71 | −2.48 | 3.43 |
All missing dummies | −0.07 | −0.03 | −0.24 | 0.01 | −0.26 | 0.03 |
Residual unexplained component | −2.45 | 8.15 | −4.36 | 6.44 | −3.04 | 5.45 |
Total raw gap | −12.68 | 22.27 | −15.56 | 20.93 | −13.31 | 19.06 |
(Sum of rows above) |
Decomposition of the test score gaps between income quintile groups: ECLS-B literacy score.
Decomposition of the test score gaps between income quintile groups: ECLS-B mathematics score.
Decomposition of the test score gaps between income quintile groups: ECLS-B language score.
Parenting differences between low- and higher-income families have been well documented, and they are associated with sizable differences in cognitive development in our analyses as in prior research (see most recently [
The
In common with prior research (see, e.g., [
Given that the USA has a largely private market in early childhood education and care, it is not surprising that large gaps in enrollment exist between lower-income and more affluent children. We consider two major domains of early childhood education and care:
Although low-income children’s enrollment in Head Start is associated with a narrowing of the gaps in school readiness, this is partially offset by their lower rates of enrollment in other types of beneficial preschool. Differential enrollment in child care (other than Head Start) accounts for between 4% and 6% of the cognitive gaps between low-income and middle-income children; differential enrollment in Head Start, in contrast, is associated with a reduction in current gaps between low- and middle-income children by between 6% and 9%.
Consistent with prior research, maternal education emerges as a moderately important factor, explaining 10 to 15% of the gaps in literacy and math readiness between low- and middle-income children in our analysis (but only 2% of the language gap). It is important to note, however, that maternal education is likely to influence directly many of the parenting and health behaviors that are included in the model. The gaps attributed here to differential maternal education, therefore, capture only the net effects of education on outcomes over and above those via the included mechanisms.
With such detailed controls in the model for what parents do and for parental education, it is perhaps not surprising that other demographic differences (in race/ethnicity, family structure, nativity, family member disability, maternal age at birth, number of children in the household, and child gender) play a fairly limited additional role in explaining income-related gaps in school readiness. These other demographic factors combined explain 16 to 19% of the gaps between low-income children and their middle-income peers, holding all else constants. The specific demographic factors that matter vary somewhat depending on which cognitive outcome we consider. The most consistent effects are those associated with differences in family size, suggesting programs that help to delay or reduce childbearing may have positive implications for child outcomes.
Taken together, the variables observed in our data can account for between 72% and 81% of the score gaps between the lowest-income and middle-income children. The remaining unexplained component, then, captures the influence of income-related differences in all the factors that we are not able to measure. Some of these omitted characteristics are likely to be unaffected by the family’s level of income even though they are correlated with it—parental cognitive ability being an obvious example. Other factors, however, such as parental stress or material hardship, are more likely to be responsive to the amount of income available.
This section focuses on the major policy levers that might reduce inequality in school readiness, taking into account what we know about the sources of inequality in early childhood as well as the likely effect of specific policies. As noted earlier, in order to reduce gaps in school readiness, policies must (1) be effective in addressing factors that are consequential in explaining the gaps and (2) do more to improve the performance of disadvantaged children than advantaged children (either because policies are targeted to disadvantaged children, or because universal policies close gaps in access to beneficial services or provide services that have a larger impact on the disadvantaged than the advantaged).
With this framework in mind, we now discuss each of the major early years policies that show promise to effectively address one or more of the factors identified above that contribute to gaps in school readiness. We distinguish between four broad categories of early years programs and also briefly discuss the role that income support and school and higher education policies could play.
As will be evident in the following discussion, early years policies may affect one or more of the factors that we found to be consequential in accounting for gaps. We illustrate this in Table
Major types of early childhood programs and their effects on factors associated with income-related gaps in school readiness.
(1) | (2) | (3) | (4) | |
Parenting | Maternal & child health | Child care | Parental education | |
I. Parental support during pregnancy/early childhood | ||||
II. Parent support & child care(children age 0–2) | ||||
III. Child care (children age 0–2) | ||||
IV. Preschool (children age 3-4) | ||||
V. Income supports | ||||
VI. Parental education |
Note:
Although home visiting programs as a group have had a mixed record of success, one specific program—the
Similarly, although parent support and parent education programs often have weak results, some well-designed and intensive programs have proved effective (in randomized trials) at improving specific aspects of parenting and/or specific child outcomes. One parenting program with a strong evidence base is the
Another promising program—the
There are also some literacy programs that have been shown to increase parents’ literacy activities with children and to improve children’s literacy outcomes. In the UK, for instance, the
In terms of health- and nutrition-focused programs, the
Thus, there is evidence that programs that provide support to parents in pregnancy and early childhood can be effective in improving factors related to parenting as well as maternal and child health. For the most part, such programs have operated to close gaps by targeting their provision to more disadvantaged parents, but in some instances (such as the Elmira Nurse-Family Partnership), provision has been universal and has operated to close gaps because effects of the program are larger for higher-risk women.
Although prior comprehensive child development programs for low-income families with young children have had disappointing results, two relatively new programs—Early Head Start in the US and Sure Start in the UK—have shown some success in improving child health and development by providing comprehensive services to low-income families. Both programs combine parent support with early child care and thus have the potential to close gaps by affecting the parenting and child care domains (as illustrated in Table
As part of the UK’s Ten Year Childcare Strategy (see [
For the most part, programs that combine parent support and early child care have aimed at closing gaps by targeting provision to high-risk families. Such programs can operate to close gaps through their effects on parenting, maternal and child health, and child care (as indicated in Table
Programs that focus primarily on delivering early child care and education to infants and toddlers have received less attention than the parent support or comprehensive programs for this age group, or preschool programs for slightly older children. In part, this reflects the strong preference that many parents in both countries have for parental care or informal child care for children in this age group, as well as the sense of many practitioners and policy developers that programs for young children should support parents as well as deliver child care and education. The limited provision for this age group also likely reflects the often contested evidence as to how early child care and education affects children age 0 to 2. In particular, while studies have shown that high-quality child care and education for infants and toddlers raises cognitive achievement, studies have also found associations between early and extensive child care and child behavior problems, particularly when care is of low quality [
Useful policies in this area, then, would focus on improving the access of low-income children to high-quality care and education, by providing more support for low-income children to attend high-quality care and education and by implementing measures to improve the quality of care and education available to them [
One challenge to be grappled with here is whether such programs should be targeted to low-income children or available more universally. For this age group, given the limited amount of resources currently available to this sector (and in light of the strong preferences many families have to arrange their own care), it probably makes sense to focus on expanding quality-contingent support for low-income families, alongside continued efforts to improve the quality of provision.
In summary, then, we would expect the main benefits of such programs to flow through improving access to care, and the quality of that care (as indicated in Table
As indicated in Table
Here, as with younger children, the question arises as to whether such programs should be targeted to low-income children or available more universally. While we favor a targeted approach for younger children, we think the case is strong in favor of universal provision for 3- and 4-year olds. Evidence on state prekindergarten programs makes a compelling case that these programs can deliver high-quality services that promote school readiness, and with larger effects for disadvantaged children. For this reason, we would emphasize universal provision of half-day prekindergarten for 3- and 4-year olds, retaining the Head Start program (with some quality improvements) to provide supplemental care and education services for low-income 3 and 4-year olds, as well as services for younger low-income children (through the Early Head Start program). We recognize that public funding for two years of prekindergarten for all children would be costly; however, all available evidence suggests that the benefits would more than outweigh the costs (see, e.g., discussion in [
The UK, of course, already provides universal nursery education for 3- and 4-year olds and is working on improving the quality, availability, and affordability of its provision as part of its Ten-Year Childcare Strategy [
Our analyses, in common with prior studies, found that even after controlling for a host of other factors, children from lower-income families lag behind other children in school readiness. We cannot determine the extent to which these differences reflect causal effects of income, and the extent to which income simply is serving as a marker for other factors. There is little direct evidence as to whether providing additional income improves parenting, parental and child health, or parental education. But we do know that income subsidies raise child care enrollment, so we have indicated that in Table
US and UK policies differ in this area (see discussion in [
While it is too soon to tell the impact of these reforms on child health and development, analyses of expenditure data reveal striking differences across the two countries. In the USA, where income gains have been tied to increased work, low-income families are spending more money on work-related items—such as adult clothing and transportation [
Given the sizable income gaps among families with young children, there is certainly scope for further income supports for low-income families. This is particularly true in the USA, where such supports are less generous and income gaps are wider. The evidence from the UK’s recent reforms is promising, in that it suggests that when benefits are labeled as being for children, parents do spend the increased income on the children.
Also relevant here are recent UK policy initiatives providing more income support to pregnant women and women with newborns through increased
There is also a considerable role for policy to play in promoting the education of the next generation of parents, as well as in attempting to redress inequality of education in the current generation. In the US, a good deal of attention is focused currently on reducing achievement gaps for students in primary and secondary school and in improving high school graduation rates (see, e.g., [
In their quest to close income-related gaps in school achievement, researchers and policymakers have begun to focus more attention on the sizable income-related gaps in school readiness that exist even before children enter school. Our analysis of contemporary birth cohort data from the US and UK suggests that this attention is warranted. In both countries, sizable income-related gaps in cognitive development are already apparent in early childhood—before children start school.
Our analysis also sheds some light on what factors might account for these gaps. While our analysis cannot show whether the factors we examine cause gaps or are simply markers for families at risk of such gaps, our accounting does provide information as to which sets of factors are more or less predictive of gaps. Income-related differences in parenting style and the home learning environment appear to be the strongest predictors, together accounting for between a third and a half of the income-related gaps in cognitive performance between low-income and middle-income children in our decomposition using the US data. Other explanatory factors include differences in maternal health and health behaviors, child health, early childhood care and education, maternal education and other demographic differences, and income itself.
What policy levers could most effectively address these gaps in the early years? The good news here is that a number of promising programs have been shown to effectively address one or more of these factors. In the parenting domain, high-quality home visiting or parent training programs such as the Nurse-Family Partnership, PALS, and PEEP have been shown to be effective at improving parenting style and the home learning environment. Both Early Head Start and Sure Start, while posting somewhat modest effects, nevertheless have outperformed earlier efforts at comprehensive early child development programs. And, the track record for preschool programs (such as Head Start and prekindergarten in the USA) is quite strong, and our estimates suggest that expansions in those programs could make a substantial difference in narrowing the income-related gaps in school readiness that we have documented. Also good news is that the most effective programs often improve more than one set of factors. Some of the best parenting programs, for instance, also improve child health or maternal health behaviors (see, e.g., the evidence on the Nurse-Family Partnership).
Of course, policymakers need to know not just what programs are effective, but what their relative costs and benefits are. Some programs that are effective in improving outcomes for disadvantaged children have been found to be cost-effective, but others have not. However, assessing the relative costs and benefits of these programs is not straightforward. Many programs have not had cost-benefit analyses because information to do so has been lacking. Moreover, even when cost-benefit analyses have been conducted, their results are not readily comparable because children have been followed for different time periods and different sets of outcomes have been tracked. A full comparison of the relative costs and benefits of these programs is beyond the scope of this paper but would be a useful next step.
In the meantime, the analysis in this paper points to several promising directions for policymakers to consider. Among these we would place the highest priority on (1) expansions in parenting-oriented programs, including those that target several aspects of parenting alongside other domains (programs such as the Nurse-Family Partnership) as well as those that focus more narrowly on specific aspects of parenting related to school readiness (programs such as PALS and PEEP); (2) continued efforts to develop and improve programs such as Early Head Start and Sure Start that have the potential to combine high-quality child care and family support for low-income children from age 0 to 2; (3) expansions in high-quality preschool programs for 3- and 4-year olds, housed in the schools or linked to them.
The authors are grateful for funding support from the Sutton Trust and Carnegie Corporation. They would also like to thank the Russell Sage Foundation, William T. Grant Foundation, National Institute of Child Health and Human Development, Pew Charitable Trusts, Spencer Foundation, Joseph Rowntree Foundation, Leverhulme Trust, Economic and Social Research Council, Social Science Research Council, Jo Blanden, Paul Gregg, and Katherine Magnuson.