research on education gap

The Hechinger Report

Covering Innovation & Inequality in Education

A decade of research on the rich-poor divide in education

Jill Barshay SENIOR REPORTER

(212)... More by Jill Barshay

Letters to the Editor

At The Hechinger Report, we publish thoughtful letters from readers that contribute to the ongoing discussion about the education topics we cover. Please read our guidelines for more information. We will not consider letters that do not contain a full name and valid email address. You may submit news tips or ideas here without a full name, but not letters.

By submitting your name, you grant us permission to publish it with your letter. We will never publish your email address. You must fill out all fields to submit a letter.

Thanks to Jill Barshay for the excellent column reminding us that there is much more to the rich/poor divide in our public schools than just the availability of digital devices and wi-fi. The real problem with equity in education is the lack of equity in school funding, which is an issue both of inequity in society and the ways in which public schools are funded (i.e., primarily local tax revenues).

Other barriers that kept the “school door blocked” for many low income students during this season of remote learning — and, presumably, next school year, as well — include: 1. Some with access to devices and wi-fi have had service disconnected at times due to unpaid (unpayable) bills. 2. Many have no private space in their homes from where to participate in synchronous learning/Zoom calls 3. With loss of family income and no child care, some have work or baby-sitting responsibilities that interfere with participation 4. Deadening effects of online learning cause many low-income students to disconnect and/or “drop out”. 5. In ability to access teacher supports and specialized instruction, esp. for English language learners and children with special needs. 6. Parent inability to assist students with computer routines, glitches, log-ins, etc

As districts address equity in the coming school year, we must also address the modes of learning that we consider both effective and valuable. If the top priority is engaging all students we need high engagement models based in trauma-informed practices, social and racial justice curricula, service learning, interdisciplinary project- and place-based learning, outdoor learning and other innovative ways to make education relevant to all students, regardless of their zip codes. Relax the standards. Cancel high stakes testing. Trust teachers to use their creativity to connect with every student and family. Otherwise, “remote” or “hybrid” learning, regardless of the availability of technology, will only be widening the gaps that structural racism has already created.

Why are we NOT reaching out to the teaching programs started by Marva Collins in Chicago and Ron Clark in Atlanta? Why are we NOT looking at a book called Schools That Work and viewing the achievements and strategies followed by successful programs. Let’s follow successful schools, successful environments in urban, rural, and suburban locations. As an eductor who started teaching in the Ocean-Hill Brownsville area of Brooklyn, N,Y. in 1971, there was a wildcat strike happening and this area was the where decentralization took place in N.Y.C. Rev. Al Sharpton’s church was down the block from I.S. 271. It took 2 years before a no nonsense, BLACK principal, took control over the choas and the movement of 125 teachers going and then coming to this “high poverty” intermediate school. There was stability of staff and the message was, you’re here to learn. I taught there for 7 incredible years and grew to understand what it was like being a minority teacher and human being. I then moved to Columbia, MD. where I lived in a planned community where diversity of color, homes, religions and belief in humanity living together as ONE took place. I taught in a white disadvantaged area for 2 years and observed the same behaviors students exhibited except there was no leadership at the top of this school. Now I teach in a suburban area for the last 31 years with limited diversity and succeeds because of innovative leadership, extraordinary teachers, and pretty high achieving students. Yes, I know every students must have access to technology as a MUST. Yes, I know urban education, rural education, and suburban education do education diffferently. Yes, I know poverty sucks, and I know distant learning may be around for a while. Change must come from the top. Let’s follow the successful educators, the successful programs, the dynamic elected officials who can shake up things so our students, our kids, our educational systems can be the change that can bring poverty to it’s knees.

I live on Long Island and know that whatever is written here about us is true. The Freeport Public School waste millions of taxpayers dollars throwing out teaching equipment, devices books that could be just given to the less fortunate schools next door-Queens, Brooklyn and the Bronx; where we see children suffering because of lack of proper learning tools. I am from the Caribbean where l taught for years. Oh l wish we were as privileged as these children. Maybe one day the disparity will end. Hopefully.

I enjoy reading this post. I am currently doing my thesis and the research question is: Do California K-12 public schools in lower-income communities offer the same level of academic curriculum as those in middle-income and wealthy communities? Do you have the reference page for those studies or even any peer reviewers where you got the information? I would like to review those studies and use them for my thesis. Thank you

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Sign me up for the newsletter!

Submit a letter

Report | Children

Education inequalities at the school starting gate : Gaps, trends, and strategies to address them

Report • By Emma García and Elaine Weiss • September 27, 2017

Download PDF

Press release

Share this page:

This report was produced in collaboration with the Broader, Bolder Approach to Education .

What this study finds: Extensive research has conclusively demonstrated that children’s social class is one of the most significant predictors—if not the single most significant predictor—of their educational success. Moreover, it is increasingly apparent that performance gaps by social class take root in the earliest years of children’s lives and fail to narrow in the years that follow. That is, children who start behind stay behind—they are rarely able to make up the lost ground.

Using data from two academic cohorts, the kindergarten classes of 1998 and 2010, this study examines the relationship between children’s socioeconomic status (SES) and their cognitive and noncognitive skills when starting school. We find that large performance gaps exist between children in the lowest and highest socioeconomic-status (SES) quintiles and that these gaps have persisted from the 1998 cohort to the 2010 cohort. The positive news is that the gaps have not grown, even as economic inequalities between these two groups of students have grown. The negative news is that the gaps have not narrowed, despite the fact that low-SES parents have substantially increased their engagement in their children’s early education.

Why it matters: These performance gaps reflect extensive unmet needs and thus untapped talents among low-SES children. The development of strong cognitive and noncognitive skills is essential for success in school and beyond. Low educational achievement leads to lowered economic prospects later in life, perpetuating a lack of social mobility across generations. It is also a loss to society when children’s talents are allowed to go fallow for lack of sufficient supports. The undeniable relationship between economic inequalities and education inequalities represents a societal failure that betrays the ideal of the “American dream.”

What can be done about it: Greater investments in pre-K programs can narrow the gaps between students at the start of school. And to ensure that these early gains are maintained, districts can provide continued comprehensive academic, health, nutrition, and emotional support for children through their academic years, including meaningful engagement of parents and communities. Such strategies have been successfully implemented in districts around the country, as described in this report, and can serve to mitigate the impact of economic inequalities on children’s educational achievement and improve their future life and work prospects.

For further discussion of policy solutions, see the companion to this report, Reducing and Averting Achievement Gaps: Key Findings from the Report ‘Education Inequalities at the School Starting Gate’ and Comprehensive Strategies to Mitigate Early Skills Gaps .

Executive summary

High and rising inequality is one of the United States’ most pressing economic and societal issues. Since the early 1980s, the total share of income claimed by the bottom 90 percent of Americans has steadily decreased, with the majority of income gains going to the top 1 percent. These trends would not be such a major concern if our education system compensated for these inequities by helping level the playing field and enabling children to rise above their birth circumstances.

But that is hardly the case. Rather, the fraction of children who earn more than their parents (absolute mobility) has fallen from approximately 90 percent for children born in 1940 to 50 percent for children born in the 1980s. And the tight links between economic inequalities and achievement gaps cast doubt on asserted equality of opportunity that promotes social mobility and puts the “American Dream” within viable reach.

Extensive research has conclusively demonstrated that children’s social class is one of the most significant predictors—if not the single most significant predictor—of their educational success. Moreover, it is increasingly apparent that performance gaps by social class take root in the earliest years of children’s lives and fail to narrow in the years that follow.

Much is known about the determinants and mechanisms that drive early skills gaps among children of different backgrounds, but our failure to narrow social-class-based skills gaps from one generation of students to the next calls for further analysis to determine the degree of influence these factors have and how interventions employed in recent years to address these factors have or have not worked and why. Moreover, shifting economic and demographic landscapes emphasize the need for more robust policy strategies to address the gaps. This three-part study thus combines a statistical analysis of early skills gaps among a recent cohort of children and changes in them over time with a qualitative study of multifaceted, school-district-level strategies to narrow them.

What we do: Questions, data and methodology

In this paper, we:

Use data from the National Center for Education Statistics (NCES): the Early Childhood Longitudinal Study of the Kindergarten Classes of 1998–1999 and 2010–2011 to measure gaps in skills by social class. To measure gaps by social class, we use the socioeconomic status (SES) metric (primarily), a composite of information on parents’ educational attainment and job status as well as household income. We compare the average performance of children in the top fifth of the socioeconomic status distribution (high-SES) with the average performance of children in the bottom fifth (low-SES). Skills measured include reading and mathematics, as well as self-control and approaches to learning as reported by both teachers and parents.
Examine SES-based gaps at kindergarten entry among the most recently surveyed cohort (the kindergarten class of 2010–2011). We study how gaps manifest in both cognitive and so-called noncognitive skills, as both skill types are important components of children’s development.
Compare these SES gaps with those of an earlier cohort (1998–1999), with a focus on changes in the skills gaps between children in the high- and low-SES quintiles. We also analyze how sensitive gaps are to the inclusion of key determinants of student performance, such as family composition, children’s own characteristics, pre-K participation, and parental and educational practices at home.
Review a set of 12 case studies of communities that have employed comprehensive educational strategies and wraparound supports to provide more children (especially low-income children) with strong early academic foundations, and to sustain and build on early gains throughout their K–12 school years.
Based on examples from these diverse communities, we discuss implications: strategies that districts can employ and district and state policy changes to make those strategies easier to adopt and more sustainable. The report ends with conclusions and recommendations for further research, practice, and policy.

What we find

Our quantitative research produces a broad set of findings:

Very large SES-based gaps in academic performance exist and have persisted across the two most recent cohorts of students when they start kindergarten. The estimated gaps between children in the highest and lowest fifths of the SES distribution are over a standard deviation (sd) in both reading and math in 2010 (unadjusted performance gaps are 1.2 and 1.3 sd respectively). Gaps in noncognitive skills such as self-control and approaches to learning are roughly between one-third and one-half as large (unadjusted performance gaps are about 0.4 sd in self-control, and slightly over 0.5 sd in approaches to learning in 2010).
SES-based gaps across both types of skills among the 2010 kindergartners are virtually unchanged compared with the prior academic generation of students (the class of 1998). The only unadjusted cognitive skills gap between children in the high-SES and low-SES fifths that changed significantly over this period was the gap in reading skills, which increased by about a tenth of a standard deviation. Gaps in approaches to learning as reported by teachers and in self-control as reported by parents shrank between 1998 and 2010 by roughly the same amount (0.1 sd). Gaps in mathematics, in approaches to learning as reported by parents, and in self-control as reported by teachers did not change significantly.
This means that though part of the SES gap is attributable to differences in these characteristics and in family investments between children in the high and low parts of the SES distribution, a substantial share of SES-related factors is not captured by these controls, but is important to explaining how and why gaps develop, and thus how to narrow them.
Moreover, the capacity for these other factors to narrow gaps has decreased over time—as a whole, they accounted for a smaller share of the gaps in 2010 than they had in 1998. This suggests that, while such activities as parental time spent with children and center-based pre-K programs cushion the negative consequences of growing up in a low-SES household, they can do only so much, and that the consequences of poverty are increasingly hard to compensate for. This resistance of gaps to these controls is thus a matter of serious concern for researchers and policymakers alike.
These children’s likelihood of attending center-based pre-K did not change significantly across generations (about 44 percent for both cohorts: 44.3 percent in 2010 vs. 43.7 percent in 1998). However, in 2010 their parents reported having a somewhat larger number of books at home for the children, and there was also an increase in both indices of activities (literacy/reading activities and other educational and engagement activities).
In addition to doing more for their children, low-SES parents have greater expectations for their children’s educational attainment—a much smaller share saw them going no further than high school graduation, while a much greater share anticipated their children attaining bachelor’s and even advanced degrees in 2010.
They were slightly more likely to live with two parents (the share not living with two parents decreased from 11.1 percent in 1998 to 9.6 percent) and to have attended center-based pre-K (the share in center-based pre-K increased from 65.8 in 1998 to 69.9 percent in 2010).
The share of high-SES homes reporting having more than 200 children’s books slightly increased in 2010, as did parents’ expectations for their children’s educational attainment.
Although research uses various indicators to measure individuals’ social class, from composite measures such as the socioeconomic status index we use to single indicators such as mother’s education or income, some sensitivity of the results to the indicator used is found. In our analyses, we find that all are equally reliable social-class proxies for the estimation of early achievement gaps, though absolute gaps and trends in them vary slightly depending on the indicator used.

Our qualitative review of community interventions also provides valuable information:

A growing number of school districts across the country have embraced systems of comprehensive enrichment and supports for many or even all their students, based on the understanding that nurturing healthy child development requires leveraging the entire community. These districts took different approaches to enacting those comprehensive strategies, based on each community’s particular mix of needs and assets, ideological leaning, available sources of funding, and other factors. But all begin very early in children’s lives and align enriching school strategies with a targeted range of supports for children and their families.
Moreover, school districts embracing what we refer to as “whole-child” approaches to education are seeing better outcomes for students, from improved readiness for kindergarten to higher test scores and graduation rates and narrower achievement gaps. They thus can provide guidance to other districts and to policymakers regarding how to implement such approaches, what to expect in terms of benefits, and which policies at the local and state levels can advance those approaches.

Conclusions

While the persistence of large skills gaps at kindergarten entry is troubling, the fact that, by and large, they did not grow in a generation—despite steadily increasing income inequality compounded by the worst economic crisis in many decades—is a good thing. But we must still be very concerned about these gaps. We would have liked to see evidence that parents’ increased dedication to and investments in their children’s early development, and increased investments in pre-K programs and other early education and economic supports, closed these gaps. However, the data suggest that these efforts simply contained them, and that these positive trends were insufficient to narrow the skills gaps at kindergarten entry. This failure to narrow gaps points to a lack of appropriate policy response at all levels of government, the neglect of decades of research across multiple disciplines on child development, and the resulting waste of critical opportunities to nurture an entire generation of children.

The policy recommendations of this report strengthen the idea that we need much greater investments in pre-K programs and continued comprehensive support for children through their academic years, including meaningful engagement of parents and communities, if we are to substantially improve the odds for disadvantaged children, in light of their extensive unmet needs and untapped talents.

Introduction: Facts about income inequality and its growth over time

One of today’s most pressing economic issues is the worrisome level of income inequality. Since 1979, the total share of income claimed by the bottom 90 percent of Americans has steadily decreased (Bivens 2016). In 1979, that 90 percent received about 67 percent of cash, market-based income (i.e., pretax income). By 2015, their share had decreased to about 52 percent of pretax income. The majority of income gains during this period went to the top 1 percent (EPI 2013; Mishel and Schieder 2016; Saez 2016). Polls reflect widespread concern about income and wage inequalities and associated trends and the desire for policies to address these inequalities ( New York Times 2015).

Rising inequality might not be such a major concern if our education, economic, and social protection systems acted as compensatory mechanisms, helping individuals, and especially children, rise above their birth circumstances and improve their mobility. But that is hardly the case. Rather, the fraction of children who earn more than their parents (a measure of what social scientists refer to as absolute mobility) has fallen from approximately 90 percent for children born in 1940 to 50 percent for children born in the 1980s (Chetty et al. 2016). Children of certain ethnic and racial minorities who are disproportionately likely to live in concentrated poverty are also more likely to do so over prolonged periods of time (Sharkey 2013). And the close connections between education inequalities and economic inequalities cast doubt on assertions that America provides “equality of opportunities” that promotes social mobility (Mishel 2015).

The influence of income inequality affects multiple aspects of society’s functioning, from health outcomes and even life expectancy to democratic ideals (Putnam 2015; Schanzenbach et al. 2016; Stringhini et al. 2017). In the education arena, children’s socioeconomic status (SES), of which income is a key component, is considered one of the most significant predictors—if not the most significant predictor—of educational success. A number of studies show the strong relationship between social class (of which socioeconomic status is a frequent measure) and test scores, educational attainment, and college attendance and completion (see Duncan, Morris, and Rodrigues 2011; García 2015; García and Weiss 2015; Lee and Burkam 2002; Mishel et al. 2012; Putnam 2015; among others).

As a result of these trends and associations, achievement gaps by social class have grown substantially since the 1960s, especially between children at the highest end of the income distribution and all of the others (Reardon 2011). Some researchers have identified a large increase in parental investment in education among high-SES parents as one driver of the divergence in education outcomes (Duncan and Murnane 2011), among other contributing factors, such as time parents spend with their children and time parents devote to education-enhancing activities (Morsy and Rothstein 2015; Van Voorhis et al. 2013): Spending on education-enhancing activities by parents in the top income fifth nearly tripled between the 1970s and the 2000s (from $3,500 in 1972 to $8,900 in 2006), while such spending by parents in the bottom income fifth remained low and changed much less (from $800 in 1972 to $1,300 in 2006) (Duncan and Murnane 2011). 1 More time can mean more frequent interactions during playtime, more time spent reading to children, and other parenting practices that contribute to children’s learning and development (Barbarin et al. 2010). In general, more leisure and educational time with children can promote their development and school readiness (Brooks-Gunn and Markman 2005; Hart and Risley 1995; Phillips 2011; Rothstein 2004; Van Voorhis et al. 2013; Waldfogel 2006). Given the evidence that parental engagement and spending directly and continuously translate into improvements in children’s achievement and preparation, the presence of the various achievement gaps are not surprising.

Education researchers and policymakers have long been attentive to issues related to equity—by race/ethnicity, SES, gender, and other characteristics. At least since the 1966 publication of the “Coleman Report” by sociologist James S. Coleman and coauthors, researchers and policymakers have understood the critical impacts of race, poverty, and segregation on educational attainment (Coleman et al. 1966). And educational inequities remain a major problem today. Rigorous research demonstrates that inequalities in both opportunity and outcomes along the lines of race and social class begin early and often persist throughout students’ K–12 years and beyond, and that they are much larger in the United States than in comparable countries (Bradbury et al. 2015; Putnam 2015). Some of the research carefully describes the specific contexts and challenges that minority and lower-social-class students face and how these challenges create early education gaps. Other studies illustrate the consequences of these gaps for children’s later learning and development (Duncan et al. 2007; Duncan and Magnuson 2011). 2 And though this body of research is smaller, a few studies have looked at trends in inequalities across cohorts (Carnoy and García 2017; Magnuson and Duncan 2016; Reardon 2011; Reardon and Portilla 2016), with mixed or inconclusive findings regarding the changes in the gaps. 3 In addition, these latter studies, however, do not address causes that could drive changes in the gaps over time. As such, there is a need both for a better understanding of these causes and for strategies to counter them.

In this paper, we describe recent skills gaps and trends in them by social class, as measured by socioeconomic status; analyze some of the major factors driving the gaps; and explore a set of diverse school district-level initiatives that are helping to narrow gaps. The paper is structured in three sections.

First, we examine social-class-based gaps at kindergarten entry among the most recently surveyed kindergarten cohort (the kindergarten class of 2010–2011). We study how gaps manifest in both cognitive and so-called noncognitive skills, as both skill types are important components of children’s development.
Next we compare these gaps with those of an earlier kindergarten cohort. We look at changes from 1998 to 2010 in the skills gaps between children in the top and bottom social-class quintiles (primarily using SES as the proxy for social class). We also analyze how sensitive gaps are to the inclusion of several key determinants of student performance, such as children’s own characteristics, family composition, and parental and education practices at home.
Then we review a set of case studies of school districts that have employed comprehensive educational strategies to provide more children (especially low-income children) with strong early academic and life foundations, and to sustain and build on early gains throughout the K–12 school years.
Finally, we look at the implications of our findings, and, based on the case study examples from diverse communities, we discuss strategies that districts can employ along with district and state policy changes that will make those strategies easier to adopt and more sustainable.

For the first two analyses, we use two nationally representative studies from the National Center for Education Statistics (NCES): the Early Childhood Longitudinal Study of the Kindergarten Classes of 1998–1999 and 2010–2011. These data provide information about children’s skills and about the children themselves, such as their race/ethnicity, socioeconomic status, language spoken at home, etc. The data also provide information on the children’s experiences in their early years, such as how actively their parents engaged them in enriching activities, whether they attended prekindergarten care, and the number of books the child has (see Appendix A). This information allows us to test the associations between children’s characteristics and their educational outcomes at school entry. For the second analysis, we draw on 12 case studies of community and school districts employing comprehensive educational strategies (Weiss 2016a–h). We explore the qualitative information provided on investments these districts have made in early childhood education, on both within-school and broader K–12 supports for children, and on evidence that these investments are delivering both improved academic achievement and broader gains for children. Based on this evidence, the report ends with conclusions and recommendations for further research, practice, and policy. Appendices A and B provide detailed discussions of the data and methodology used in this paper.

How large are recent performance gaps at kindergarten entry?

This section documents inequalities among the most recently tracked cohort of students as they entered kindergarten in 2010. It provides us with the most recently available view of the various aspects of gaps at the school starting gate, all of which are critically important for understanding the implications of those gaps. The findings below draw on the Early Childhood Longitudinal Study of the Kindergarten Class of 2010–2011, and we use data from the fall measurement in the kindergarten year. (This section partly builds on our previous work; see García 2015 and García and Weiss 2015. See Appendices A and B for details on the variables and methodology used.)

Our decision to examine performance in both cognitive and noncognitive skills reflects growing acceptance that children’s development is a complex process in which both skill types build on and interact with each other, and on evidence of the roles that both types of skills play in the education process and adulthood outcomes (see García 2015; García and Weiss 2016; Levin 2012a, 2012b). Traits and skills such as critical thinking, creativity, problem-solving, persistence, and self-control are vitally important to children’s full development, and are nurtured through life and school experiences. These skills, sometimes referred to as noncognitive or social and emotional skills, tend to develop—or lag—in tandem with cognitive skills. Noncognitive or social and emotional skills are thus linked to academic achievement, and also to outcomes in adult life, such as productivity and collegiality at work, good health, and civic participation.

For these analyses, we use a measure of socioeconomic status that has three components: the educational attainment of parents or guardians, parents’ occupational prestige (determined by a score), and household income (see more details about the SES construct in Tourangeau et al. 2013, 7-56 to 7-60). We divide children of the 2010–2011 kindergarten class into five groups based on SES quintile. To measure the gaps in performance by socioeconomic status, we compare the average performance of children in the top fifth of the SES distribution with the average performance of children in the bottom fifth. This provides an estimate of the relative advantage of a child in the top fifth of the SES distribution (referred to in this report as “high-SES”) with respect to a child in the bottom fifth (“low-SES”).

Children are not equally prepared for school when they enter kindergarten, and our analyses show that students’ social class strongly determines their relative position in the performance distribution. Most socioeconomically disadvantaged children lag substantially in both reading and math skills, and these skills levels rise along with socioeconomic status (sometimes referred to as socioeconomic gradients). Children in the highest socioeconomic group score significantly higher in reading and math than children in the lowest socioeconomic group. As Table 1 shows, the relative unadjusted gaps in reading and math, i.e., the advantages of high-SES children relative to low-SES children in 2010 are 1.17 and 1.25 sd, respectively (Table 1 also shows that, after controlling for clustered data, the gaps are 0.94 and 0.91 sd, respectively). 4 Reading and math skills advantages of children in the middle of the SES distribution relative to the lowest SES group are roughly half as large as the advantages of high-SES children to the lowest SES group. 5

Children in the lowest socioeconomic quintile also lag substantially in noncognitive skills, based on assessments by both parents and teachers, although these gaps are smaller than those in reading and math. Socioeconomic-based gaps in self-control and approaches to learning are approximately one-third to one-half as large as gaps in reading and math. 6 In 2010, children in the high-SES quintile scored 0.38 sd and 0.51 sd higher in self-control and approaches to learning as reported by teachers (0.36 sd and 0.56 sd after clustering; see Table 1) than children at the low-SES quintile (see Figure A ). Using parents’ assessments of the same skills, the gaps are 0.39 sd and 0.56 sd, respectively (0.33 sd and 0.46 sd after clustering; see Table 1).

Our analyses also document stark socioeconomic disparities in inputs, child and family characteristics, and other factors that can affect school readiness ( Table 2 ). Here too we find a correlation between socioeconomic status and other factors that impede educational development. Low-SES students are more likely than their high-SES peers to be immigrants and less likely to speak English at home, to live with two parents, to have participated in center-based pre-K care activities in the previous year, and to have engaged in early literacy practices at home. Among children in the low-SES group, half (50.4 percent) are Hispanic, 23.1 percent are white, 19.6 percent are black, and 2.5 percent are Asian. 7

Though these gaps in both cognitive and noncognitive skills are troubling and call for policy recommendations, better policy solutions can be designed if we understand how these gaps have changed over time and what factors have played a role in those changes. Education outcomes are the product of a combination of multiple factors, which can reinforce or mitigate relative advantages or disadvantages in a dynamic fashion. We examine these issues in the rest of the paper.

How do the performance gaps in the 2010–2011 kindergarten class compare with the gaps in the prior generation?

The analyses presented in this section compare the inequities in inputs and the performance gaps between high-SES and low-SES students who began kindergarten in 2010 with the gaps among high-SES and low-SES schoolchildren in the prior academic generation, the 1998 cohort. We also analyze factors that have had major influences on the changes in performance of kindergartners, and briefly discuss the research and policy implications of our findings.

How have the characteristics of the children in the lowest and highest SES groups changed in a generation?

We first analyze children’s characteristics by SES quintiles in the two cohorts. This enables us to identify differences in the characteristics of low-SES kindergartners in 2010 versus in 1998. These changes may help explain why the performance gaps we are studying grow or shrink (for example, if children in the low-SES quintile in 2010 were more likely than their 1998 peers to have access to public programs such as pre-K, they might be more prepared for kindergarten, and thus the relative advantage of high-SES children might shrink). 8

Table 2 shows the student and family characteristics of the kindergarten classes of 1998–1999 and of 2010–2011, by SES quintile. The table also includes pre-K care arrangements and two indices of developmental activities parents undertake with their children—indices of “literacy/reading activities” and “other activities”). 9 The table also summarizes parents’ expectations regarding their children’s educational attainment. To some extent, expectations are based on hope, but they can also respond to behavioral patterns children are exhibiting that hint at their future success. Expectations can also influence outcomes by representing how motivated parents are for their children’s education. The ECLS-K survey does not ask parents how their expectations (and changes in their expectations) affect their provision of educational activities or support, but their answers to the expectations question can be used as a reasonable proxy of the degree to which parents are aware of their children’s education and willing to support it. 10

The most significant changes in children’s characteristics by SES quintile are for children in the bottom of the distribution. In 2010, a greater share of children in this group are Hispanic (50.4 percent, an increase of 10.6 percentage points relative to the 1998 share of 39.8 percent), live in homes where the main language is not English (40.3 percent, an increase of 9.1 percentage points from 31.2 percent in 1998), and are immigrants (49.8 percent, an increase of 19.5 percentage points from 30.3 percent in 1998). In 2010, a greater share of children do not live with two parents (54.9 percent, an increase of 9.3 percentage points from 45.6 percent in 1998), and live in poverty (84.6 percent, an increase of 13.3 percentage points from 71.3 percent in 1998). These substantially greater disadvantages for children at the bottom of the SES scale could all be reflections of both the much weaker national economic context in 2010 versus 1998 and the growing inequality described above.

These children’s likelihood of attending center-based pre-K did not change significantly across generations (about 44 percent for both cohorts), but they were more likely to be looked after by parents or relatives (with the share increasing from 46.4 percent in 1998 to 50.9 percent in 2010). These children’s parents also reported having a somewhat larger number of books at home for the children, and there were increases in their indices of educational and engagement activities (two composite measures, with the literacy/reading index measuring how frequently parents read books to their child, tell stories, sing songs, and talk about nature and how frequently the child reads picture books and reads outside of school, and the “other” index measuring how frequently parents and children play games or do puzzles, play a sport or exercise together, and build something or play with construction toys; and how often parents help children do arts and crafts and involve children in household chores). These parents’ expectations about their children’s educational attainment also changed significantly: the share who expected their children to attain no more than a high school diploma decreased by more than half (from 24.1 percent in 1998 to 11.4 percent in 2010), and the share of parents who expected their children to attain at least a bachelor’s degree increased, markedly for those expecting their children to obtain an advanced degree (a master’s degree, Ph.D., or M.D.).

Among children in the high-SES quintile, the group in 2010 includes a lower share of white children (falling from 78.8 percent in 1998 to 71.3 percent) and a larger share of Asian children (increasing from 4.7 percent in 1998 to 8.7 percent). Children in the high-SES group became slightly more likely to live with their two parents (the share of children who lived with one parent decreased from 11.1 percent in 1998 to 9.6 percent), and to have attended center-based pre-K (65.8 percent in 1998 and 69.9 percent in 2010). We only see a small increase in the reported number of books at home. 11 The share of homes reporting having more than 200 books—the maximum—increased slightly in 2010, across all SES quintiles except for the middle quintile). As was true of low-SES parents, those in the highest quintile raised their expectations for their children’s educational attainment from 1998 to 2010. Compared with the 1998 cohort, a larger proportion of high-SES children in the 2010 cohort were expected by their parents to attain an advanced degree (master’s degree or higher), while a lower share expected their children to attain a bachelor’s degree only.

How did the performance gaps between the children in the lowest and highest SES groups change in a generation?

Changes over time in the input factors by socioeconomic status (child and family characteristics, early-education practices, and parents’ expectations) explored above have been found by researchers to have major impacts on the outcomes (test scores on reading and math, and measures of noncognitive skills) explored in this section. 12 In other words, we would expect that changes in the unadjusted skills gaps (gap measures that do not include controls for child and family characteristics, early-education practices, and parents’ expectations) would partially reflect the compositional differences between the class of 2010–2011 and the class of 1998–1999. For example, we would anticipate that if the more recent generation’s low-SES parents read to their children more frequently, helped them do more arts and crafts, or had higher expectations for them, these factors would correlate with narrowing skills gaps. Also, we would expect that the adjusted skills gaps (gap measures that are net of the influence of child and family characteristics, early-education practices, and parents’ expectations, and thus reflect the SES gaps) would be different for the two cohorts if the correlations between inputs and outcomes had changed over time or if the share of children’s outcomes the adjustments account for had changed over time.

To understand these factors’ potential influence on gaps, we examine both unadjusted and adjusted gaps in the tables in this section. We also examine gaps by some of the components of the SES index, such as household income or mother’s educational attainment, and by other variables that are sometimes used as proxies of the child’s socioeconomic background, such as number of books in the home. If the gaps by SES components and proxies somewhat differ, this tells us that researchers’ choices about how to divide children into groups and compare them matter—both for their findings and for their policy recommendations.

Table 3 shows the unadjusted and adjusted gaps between the standardized scores in reading and math of kindergarten children in the top SES quintile relative to the bottom SES quintile in 1998 and the change in that gap by 2010. 13 Table 4 performs the same analysis for gaps in measured noncognitive skills. The tables show two somewhat perplexing patterns. On the one hand, the cognitive and noncognitive skills gaps between high-SES and low-SES children are large and statistically significant in both cohorts. But while significant social-class-based performance gaps persist from one kindergarten generation to the next, there is not the same consistency in how the high-SES to low-SES gaps change. For some cognitive and noncognitive skills, the performance gaps grow, while for others the gaps shrink, or remain the same from one generation to the next (which may complicate the process of understanding why performance gaps have changed over time).

Beginning with our unadjusted model (data column one), the only substantial increase in the gap between high- and low-SES children from 1998 to 2010 was in reading skills, which increased by one-tenth of a standard deviation. There were no significant changes in gaps in math skills, which, as the literature indicates, are less sensitive than reading skills to parents’ activities at home (see Rothstein 2004, 2010). Similarly, gaps in approaches to learning as reported by parents and in self-control as reported by teachers did not change significantly, and gaps in approaches to learning as reported by teachers and in self-control as reported by parents shrank by roughly the same amount as the reading gap (about a tenth of a standard deviation—0.12 and 0.08 sd, respectively). Figure A provides a graphic illustration of the unadjusted gaps in cognitive and noncognitive skills of high- and low-SES children across the two cohorts.

The additional models estimated for each outcome and shown in Tables 3 and 4 offer other key findings. In Model 1, we used the full samples for the two cohorts but did not include any controls that capture characteristics of children or their parents or the early education practices in which families engage. Model 2 partitions the data into schools and classes, or clusters, so that the subjects in the clusters are more similar to one another than to those in other groups. Under this adjustment, the gaps shrink substantially, by between 15 and 25 percent across the skills, and the regression fit improves significantly (see increased adjusted R-squared, i.e., this model explains more of the total variation in the outcomes than the first model). This clustering takes into account school segregation, that is, that children are not randomly distributed but tend to concentrate in schools or classrooms with children of the same race, social class, etc. Clustered estimates provide a comparison of the skills gaps of peer students—those in the same schools and classrooms—rather than a comparison across schools. García (2015) and Magnuson and Duncan (2016) offer these estimates too.

How do child and family characteristics, activities, and expectations affect SES-based performance and performance gaps?

We next examine the contribution of the certain variables of interest to SES-based performance gaps. We approach this in two ways. First, we examine the changes in the gaps (Tables 3 and 4, Models 3 and 4) and the overall reduction in the gaps that results from controlling for children and their family characteristics, early literacy practices, and parental expectations of educational achievement ( Table 5 ). Second, we assess the influence of select early educational practices on performance and how that influence has changed over time by looking at the associations between these inputs and performance ( Table 6 ).

Models 3 and 4 in Tables 3 and 4 use the samples that result from removing observations without full information for the controls of interest. 14 Adding controls is important because performance gaps based on socioeconomic status may be explained by differences in variables other than the child’s socioeconomic status. In other words, we aim to determine which part of the gap is attributable to children’s SES, net of other factors that matter for performance. Thus, in the third data column (Model 3), we add controls for individual and family characteristics (gender, race/ethnicity, whether English is the primary language spoken at home, disability, age, whether children live with two parents) and early educational and play activities (center-based pre-K care, indices for literacy/reading activities and other activities, and total number of books the child has). Model 3 also includes the interactions between the early education variables with time. 15 In the fourth data column (Model 4), we control for the same factors as in Model 3 but add controls for parental expectations of children’s educational attainment (whether they expect their children’s highest level of education attained will be high school diploma or less, some college or vocational studies, bachelor’s degree, or advanced degree) and their interaction with time. 16 We describe these results in the next section.

Including covariates changes the estimates of SES-based skills gaps in various ways. First, the gaps between the top- and bottom-SES quintiles shrink, showing that SES-based gaps are partially explained by the variation in the controls (which is not visible in the tables). 17 Second, controls do not significantly change the SES-based gaps over time, in general; i.e., the coefficients associated with changes in the gaps between high- and low-SES children remain almost the same, or change very minimally, depending on the skill measured. The statistical significance of the SES-based skills gaps in 1998 is not affected by the inclusion of the controls (see rows “Gap in 1998–1999” in tables), but the statistical significance of the changes in the gaps between 1998 and 2010 (see rows “Change in gap by 2010–2011” in tables) is somewhat affected by the inclusion of the controls (note that the sizes of the coefficients measuring gaps in 1998 change after the inclusion of the controls, but that the sizes of the coefficients measuring changes in them between 1998 and 2010 do not change significantly). In reading, the change in the gap between 1998 and 2010 diminishes and becomes statistically insignificant in the last model (the relative gap increases by 0.08 sd but this change is not statistically significant), meaning that adding parental expectations of education accounts for some of the increase in the gap detected in Models 1 to 3. The only SES-based skills gap that shows a statistically significant increase from 1998 to 2010 once parental expectations are controlled for is the gap associated with parents’ assessment of approaches to learning, which increases by 0.11 sd. Gaps between high- and low-SES children in cognitive and noncognitive skills after adjustments are made are shown in Figure B .

As mentioned above, the fact that the skills gaps decrease after controls are taken into consideration affirms that SES-based gaps are due in part to variation in the controls among high- versus low-SES children. This trend can be seen in Table 5, which, as noted above, shows the overall reduction in gaps that results from controlling for child and family characteristics, early literacy practices, and parental expectations of educational achievement. With respect to cognitive skills, the 1998 gaps shrink by 46 percent and 53 percent, respectively, after the inclusion of the covariates. About half of the gaps are thus due to other factors that are associated both with SES status and with the outcomes themselves. The reduction in the 1998 gaps for noncognitive skills varies from 28 percent (approaches to learning as reported by teachers) to 74 percent (approaches to learning as reported by parents). (For self-control as reported by teachers, the reduction is 51 percent versus 35 percent when reported by parents.)

While the gaps hold after the inclusion of controls across outcomes, gaps in 2010 are less sensitive to the inclusion of the covariates than they were in 1998. This trend can also be seen in Table 5. 18 Declining values from 1998 to 2010 indicate that factors such as early literacy activities and other controls are not, as a group, explaining SES-based gaps as much as they had a decade prior. This change could be due to the failure of the index to fully capture parents’ efforts to nurture their children’s development and/or the index becoming somewhat out-of-date. In any event, the resistance of gaps to these controls should worry researchers and policymakers. The waning influence of these controls makes it harder to understand what drives SES gaps. It also suggests that the gaps may be growing more intractable or, at least are less easily narrowed via the enactment of known policy interventions.

Finally, we examine the association of performance outcomes (not performance gaps) with selected early educational practices, including having attended center-based pre-K, literacy/reading activities and other activities, and total number of children’s books in the home (Table 6). 19 We are mainly interested in two potential patterns: whether these factors are associated with outcomes (and, if so, how intense the associations are), and whether the relationships have changed over time.

In keeping with established research, having attended center-based pre-K is positively associated with children’s early reading and math skills. For 1998, the estimated coefficients are 0.11 sd for reading skills and 0.10 sd for math skills, substantial associations that do not change significantly over time. In other words, attending pre-K in 1998 improved kindergartners’ reading skills by 0.11 sd and improved kindergartners’ math skills by 0.10 sd relative to not attending pre-K. However, while center-based pre-K continues to reduce self-control as reported by teachers in 2010, the effect is less negative in 2010 (the 0.06 improvement from 1998 to 2010 shown in the bottom panel of the table shows us that the effect in 2010 was -0.07 [-0.13 plus 0.06], compared with -0.13 sd in 1998). We find no independent effect of center-based prekindergarten schooling (i.e., no effect in addition to SES, in addition to other individual and family characteristics, or in addition to other SES-mediated factors), on approaches to learning or on self-control as reported by parents. 20

The number of books children have at home likewise supports their skills at the beginning of kindergarten. Indeed, this factor is positively associated with all outcomes but self-control reported by parents. The coefficients are very small, of about 0.01 to 0.02 sd (associated with changes in outcomes for each 10 additional/fewer books the child has, as expressed by the continuous scale with which number of books in the home is measured, which is divided by 10 for the analyses (as mentioned in Appendix A), and these relationships do not change over the time period.

The two types of parenting activities that are summarized by the indices “reading/literacy activities” and “other activities” show interesting correlations with performance and patterns over time. On the one hand, the “reading/literacy activities” index (a composite of how frequently parents read books to their child, tell stories, sing songs, and talk about nature, and how frequently the child reads picture books and reads outside of school) is strongly and positively associated with all outcomes other than children’s self-control as reported by the teacher. The associations with cognitive skills, especially with reading, are strong and statistically significant—0.17 sd for reading performance and 0.07 sd for math—and these associations did not change significantly between 1998 and 2010. For noncognitive skills, the relationships are strong for those assessed by parents, though they shrink by about half over time: self-control is 0.14 sd in 1998 and decreases by 0.08 sd by 2010; approaches to learning is 0.32 sd in 1998 and decreases by 0.17 sd by 2010). The relationship is much weaker, though still statistically significant, for teachers’ assessed approaches to learning (it is 0.03 sd in 1998 and does not change significantly by 2010).

On the other hand, the index that measures other enrichment activities that parents do with their children (a composite of how frequently parents and children play games, do sports, build things, work on puzzles, do arts and crafts, and do chores) shows significant correlations with all of the skills, but they may be either positively correlated or negatively correlated, depending on the skill. For cognitive skills, the associations are statistically significant and negative, though stronger and somewhat more meaningful or more intense with reading achievement (-0.12 sd in 1998) than with math achievement (-0.04 sd). 21 These associations did not intensify nor weaken over time. For noncognitive skills the associations are highly positive and statistically significant, and very strong for parents’ assessment of approaches to learning (0.29 sd in 1998). As explained by García (2015), these correlations between “other activities” and noncognitive skills as assessed by parents could be bidirectional: engaging children in enrichment activities might enhance their noncognitive skills, but, at the same time, parents who are more inclined to participate in their children’s early play and educational time are probably more likely to perceive or judge that their engagement has an impact on their children’s skills. But the fact that both the frequency with which parents engage in most of these activities and the importance of this index for parent-assessed skills increased noticeably from 1998 to 2010 (by 0.22 sd for self-control and 0.27 sd for approaches to learning) suggests that parents are growing more informed and involved in their children’s early education over time. It also indicates that parents are increasingly acting on this knowledge and that this involvement will continue to grow, albeit potentially with decreasing marginal returns to time and resources invested. The association between “other activities” and teachers’ assessments of children’s noncognitive skills is also positive but weaker than that of parents’ assessments (about 0.03 sd for approaches to learning and 0.05 sd for self-control), and remained unchanged during the time period studied.

Finally, we find a strong association between parental expectations for their children’s educational attainment and all measured skills. In other words, net of socioeconomic status, the higher the expectations, the higher cognitive skills children have, and the higher the assessments by parents and teachers of children’s noncognitive skills. The parental expectations portion of the table measures children’s performance relative to children whose parents’ expectations are the lowest (high school diploma or less). While the expectation that a child will pursue some vocational education or complete college has a statistically positive influence on all skills measures except for reading, the expectation that their children will complete a bachelor’s degree or more education has a stronger influence, including on reading skills: between 0.11 to 0.16 sd higher in reading and between 0.17 to 0.22 sd higher in math in 1998. High expectations for children’s educational attainment also have a statistically positive effect on noncognitive skills. When the expectation is for an advanced degree (master’s or higher), coefficients vary from 0.12 sd in self-control by teachers to 0.38 sd in approaches to learning by parents in 1998. In addition, most of these associations—particularly the cognitive gradients—grow in 2010. Relative to children whose parents have low expectations, children whose parents have the highest expectations for their children’s attainment (graduate studies) perform much better in reading and math than in 1998 (relative gaps grow by 0.19 and 0.12 sd respectively). A similarly stronger association is noted for noncognitive skills assessed by teachers (though not for parents’ assessments of their children’s skills).

Sensitivity analyses: Do performance gaps vary based on which proxy for social class (socioeconomic status) is used?

Part of the challenge to making conclusive statements about trends in education gaps by social class is the existence of multiple valid proxies for measuring children’s social class or socioeconomic status. 22 Although researchers treat these proxies as equivalent, and even interchangeable, the lack of a comparison of results obtained using various indicators limits our capacity to extract major conclusions on social-class trends and their drivers, and hence hinders the plausibility and effectiveness of the policy recommendations that build on any specific indicator’s findings (net of other methodological and instrumental differences that may exist across studies).

We thus conduct analyses using several of the main proxies employed to measure socioeconomic status. The purpose of these analyses is twofold. The first purpose is to test the sensitivity of the estimated relative gaps, and of trends in them, to changes in the measurement of this key predictor of education performance. (In other words, if all the indicators are reliable proxies of SES, gaps and trends obtained using the various metrics should be similar.) The second purpose is to increase the comparability of the results of studies addressing trends in education inequalities that use various metrics of social class. This is an important issue; in addition to helping reconcile diverse results found in the literature, these analyses may reveal why patterns differ, and have significant policy implications.

As such, instead of the SES composite measure we use to estimate SES-based gaps in this report, we use three alternative indicators to run our analyses: mother’s educational attainment, household income, and number of books the child has in the home. Unlike the SES composite measure, two of these measures offer the advantage of being directly comparable over time. Both mother’s educational attainment and number of books the child has are objective categories. As a limitation, and mainly associated with the information that is available in the raw data, none of these categories can be transformed into a percentile-variable without major transformations. (The adjustments to ensure comparability over time are explained in Appendix A. See Reardon and Portilla 2016 for an analysis with a transformation of the income variable that offers a proper percentile comparison, based on the methodology developed by Reardon 2011.) Still, they are variables associated with social class and can be ordered in groups or categories that identify high- and low-social-class statuses. Thus, with the necessary caution when interpreting and using the findings, we offer this comparison of results as a sensitivity analysis.

We create five categories with these indicators, maintaining the structure of comparing “high-SES” (top quintile) with “low-SES” (bottom quintile) as in Tables 1–5 (note that we are using “SES” interchangeably with “social class” here). For simplicity, Tables 7–9 show only the results from two models: one without covariates (Model 1, baseline estimates) and one with all covariates (Model 4, fully adjusted estimates). We focus on the findings for the baseline relative gaps in 1998 and 2010 first ( Figures C–E ). The overall patterns found in the results suggest that all social-class gaps are statistically significant and sizable. However, the exact sizes of the gaps vary depending on the social-class indicator used and the outcome being assessed. Also, the changes in the gaps over time vary depending on the indicator used to capture children’s social class.

In addition to these general findings, we note some more detailed ones. For 1998, gaps by mother’s educational attainment (Figure C; Table 7) are the largest across all indicators (except for the gap in self-control as assessed by teachers, which is slightly smaller than the gaps as measured using household income and number of books the child has), while gaps by number of books (Figure E; Table 9) are the smallest across all indicators (except for the gap in approaches to learning as assessed by parents, which is slightly larger than the gap for household income). Again, according to the 1998 data, the coefficients of gaps by mother’s educational attainment are generally larger—and in three cases much larger—than those obtained using number of books in the home as the indicator of social class. For example, the relative gap is 1.29 sd in reading and 1.46 sd in math when mother’s education is the SES proxy, compared with gaps of 0.74 sd and 0.97 sd when number of books in the home is the SES proxy.

It is also important to note that gaps by mother’s educational attainment (Figure C; Table 7) and income (Figure D; Table 8)—two of the five components of the SES construct—are very close to the ones obtained by our SES composite measure (as shown in Figure A). All in all, results seem internally consistent as well as generally consistent with prior results on this topic (Reardon and Portilla 2016).

In terms of changes in the performance gaps over time (unadjusted), the findings vary depending on which indicators of social class are used, with mother’s education and household income being the indicators associated with the largest changes in the gaps. Changes in the performance gaps in cognitive skills between 1998 and 2010 by our composite SES measure and books are similar: an increase in the reading gap between children in the top and bottom quintiles of about a tenth of a standard deviation (0.10 sd with the composite SES measure [Figure A] and 0.08 sd if SES is proxied with books), and no significant change in mathematics (there are some differences in the noncognitive outcomes).

However, by mother’s educational attainment, there are no changes in relative reading and approaches to learning gaps reported by parents over time, and a significant reduction in the gaps in the remaining outcomes. Meanwhile, income-based gaps for the two cognitive skills—reading and math—decreased by -0.13 and -0.23 sd respectively, and for approaches to learning as reported by teachers by -0.13 sd. No significant changes occurred for the remaining noncognitive skills.

In sum, this sensitivity analysis demonstrates that all of the indicators are reliable proxies of SES for the estimation of early achievement gaps, though absolute gaps may vary slightly depending on the indicator used. However, the proxies are not equally reliable when we assess trends in the gaps by SES or their drivers. As such, aside from differences in the definitions and procedures used to construct each SES proxy, the proxies should not be treated as fully equivalent. The decomposition conducted here helps clarify the different weights that various components of SES may have in driving changes in gaps by social class. For example, variation in income across groups over time is associated with decreased performance gaps in the cognitive skills between 1998 and 2010, and variation in educational attainment quintiles or categories over time is associated with decreased performance gaps across cohorts in most noncognitive skills. But variation in books in the home over time and among groups is associated with increased gaps in reading and in parents’ assessed approaches to learning. Such findings also point to very different policy solutions: if mothers’ education is the main driver, enhancing that will improve children’s prospects. On the other hand, findings that indicate that income inequality is the larger culprit would point to the need for policies that reduce such inequalities. Future research should consider and look more closely into these questions.

What can we learn from these analyses?

The multiple factors and relationships examined in this section can now be examined from a policy perspective. If the aim is to increase equity, to improve children’s development across the board, and to improve our understanding of children’s development, there are two major policy recommendations:

Directly support less-resourced families so that they have greater access to educational and economic resources (for the latter, see García and Weiss 2017). All the early educational and play activities measured, which include center-based pre-K care and literacy/reading and other activities, as well as the number of books a child has, are positively associated with children’s readiness, and in part account for social-class gaps, but are much less accessible to children of lower socioeconomic status. Virtually all of the associations between these factors and outcomes were strong and positive (with a handful of exceptions), and some even grew over time. A related research recommendation of particular interest would be to examine whether the intensity of these activities or practices has any threshold level of effectiveness (after which point they no longer affect children’s development). 23 Also, it would be helpful to understand why parents’ expectations of their children’s educational attainment increased so much and how this has affected children’s development. For example, do parents have a better understanding of the relationship between educational attainment and prospects for success in life and the workforce? Are children performing better because their parents expect more, or because parents who expect more are also delivering more in the form of enriching activities?
Design and implement strategies that compensate at the community level for children’s lack of access to key foundational resources (economic and educational). These strategies can be considered indirect supports for less-resourced families that reduce inequities and complement the direct supports described above. Examples of communities that have enacted such comprehensive support initiatives provide a good starting point to explore how and why they emerge; the types of supports they provide (from preschool programs and home visits with parents to enriching summer programs, school-based health clinics, and more); the challenges of scaling them up and sustaining them; the benefits they deliver for students, and particularly for disadvantaged students; and their implications for policy at the local, state, and even federal levels. The next section of this report thus presents an analysis based on qualitative data from promising initiatives in a dozen school districts across the country (Weiss 2016a–h).

What are pioneering school districts doing to combat these inequities and resulting gaps?

This section of the report draws on a set of case studies published by the Broader, Bolder Approach to Education (BBA), a national campaign that advances evidence-based strategies to mitigate the impacts of poverty-related disadvantages on teaching and learning. 24 The case studies feature school districts that have employed comprehensive educational strategies to ensure that more children, especially low-income children, have strong early academic and life foundations, and that resulting early gains are sustained and built on through children’s K–12 years. (These strategies are often referred to as “whole-child” approaches to education, in reflection of their holistic nature.) We explore the premise that school districts that take a whole-child approach to education and a whole-community approach to delivering it are likely to enjoy larger gains in academic achievement and to narrow their race- and income-based achievement gaps. In doing so, we are building on evidence suggesting that consistent, strong supports for children and their families—both in and out of school—can avoid the “fade-out” seen among graduates of many pre-K programs and even enhance those programs’ early benefits.

This section is thus divided into four parts: (1) an introduction to the case study districts, followed by discussions of (2) how these districts invest in early childhood care and education, (3) how the districts’ investments in K–12 strategies sustain and boost the early childhood investments, and (4) how academic gains and narrowing achievement gaps indicate that the investments are paying off. Table 10 provides basic information on the 12 school districts/communities studied; Appendix E at the end of this report provides more information on key characteristics of these districts. 25

Introduction to the case studies: Why these districts enacted whole-child strategies

Large and growing disparities in the economic well-being of children in America and extensive evidence linking those disparities to widely diverging educational outcomes have prompted action among a growing number of communities and school districts. Heeding the evidence that out-of-school factors play even larger roles than school-based factors in school performance, these districts are seeking ways to mitigate the poverty-related impediments to effective teaching and learning.

These districts have benefited from a substantial body of research on strategies with promise to address core challenges that students and schools face—strategies that have been shown to shrink achievement gaps by narrowing major disparities in opportunity (Carter and Welner 2013). The first, and perhaps best-documented, of these strategies is high-quality early child care and education, especially when it engages parents early and in meaningful ways. High-quality early childhood education programs not only narrow achievement gaps at kindergarten entry but also deliver long-term benefits to children, their families, and society as a whole (Chaudry et al. 2017; Rolnick and Grunewald 2003).

Programs that support students’ physical and mental health and improve their nutrition are also known to reduce chronic absence and keep students focused and learning, and thus improve their academic performance (CDC 2016). Well-designed after-school and summer-enrichment programs likewise boost achievement, both directly and indirectly by enhancing students’ engagement in and attachment to school (Peterson 2013).

Whole-child approaches integrate these and other strategies into a comprehensive set of aligned interventions, leveraging the whole community’s resources to meet the broad range of student needs. While the impact of such comprehensive approaches has not been studied as extensively as the individual components, considerable theoretical and emerging empirical research point to the strong potential of such strategies to boost achievement and narrow gaps (Child Trends 2014; Oakes, Maier, and Daniel 2017; Weiss 2016i).

This section of the report seeks to add to that knowledge base by sharing qualitative information on how such comprehensive approaches have emerged and grown, what they look like when they are successfully implemented, and what types of outcomes and benefits result and how outcomes vary across diverse communities.

How are whole-child initiatives launched?

Each of the districts studied has distinct circumstances, and thus distinct reasons for coming to the conclusion, as a community, that it needed to take a comprehensive approach to education. At the same time, demographic trends that are affecting virtually every state—and many, if not most, school districts across the country—have played major roles in that decision in every case. 26 Indeed, community and school leaders in all of these districts cited students’ poverty (and, in some districts, demographic shifts) as posing challenges that required looking beyond the school walls to address.

How these factors triggered the initiative’s launch varied, but poverty was at the core in each community’s decision. For example, in 2008, community leaders identified East Durham as one of Durham, North Carolina’s, most distressed areas, based on a community risk assessment conducted by Duke University’s Children’s Environmental Health Initiative. The 120-block area’s 11,000 residents had a 40 percent poverty rate and a homeownership rate of just 19 percent, along with high rates of crime and unemployment, putting its 3,000 children and youth at high risk of academic failure (Weiss 2016e).

Across the country, in Vancouver, Washington, the share of children eligible for subsidized school meals rose from 39 percent to over 50 percent in less than a decade, such that, by 2015, in some central-city schools, more than four in five students qualified for subsidized school meals in 2015 (Weiss 2016b). In another distressed community, in north Minneapolis, median family income was just $18,000 in 2011, and fully one-fourth of the 5,500 Northside students were homeless or “highly mobile” (in such unstable housing that they were at risk of homelessness) (Weiss 2016d). In Pea Ridge, Arkansas, schools “had difficulty finding resources that met the needs of kids,” says superintendent Rick Neal. “We knew that we were not identifying all the needs that were there. I think that’s the way a lot of districts are” (Weiss 2016f). And in the early 1990s, the Tangelo Park neighborhood in Orlando, Florida—an isolated enclave of 3,000 residents, almost all low-income and African American—caught the attention of hotelier and philanthropist Harris Rosen, who was looking for a neighborhood in which to invest (Alvarez 2015).

Each of these districts took different approaches to enacting those comprehensive strategies, based on the community’s specific mix of needs and assets, ideological leaning, available sources of funding, and other factors. One of the most politically progressive of the districts studied, Montgomery County Public Schools (MCPS) in Maryland, paved the way for a whole-child approach in the early 1970s when it enacted housing policy that uses mixed-income residential developments to create communities with families of different income levels. In the 1990s, the county developed Linkages to Learning, a “community schools”–type approach targeted to engaging and partnering with low-income and immigrant parents and families and connecting them with a broad range of community resources (MCPS 2016). (Community schools are known for building partnerships with community agencies and private service providers to meet student and family needs.) Austin Independent School District (AISD), also in a politically progressive jurisdiction, began its whole-child efforts through parent- and community-organizing in schools. It has since invested in social and emotional learning and in a community schools strategy (CASEL 2017).

At the other end of the spectrum are whole-child approaches in Joplin, Missouri, and Pea Ridge, Arkansas, districts located in more politically conservative southern states. These districts operate under the umbrella of Bright Futures USA (a spinoff national nonprofit that began with Joplin’s Bright Futures initiative). The Bright Futures districts take a more individualistic angle, asserting that every member of the community has “time, talent, or treasure” to offer that can help children overcome disadvantage and ensure more equal opportunity (Weiss 2016a).

Two other districts have modeled their efforts on the Harlem Children’s Zone (HCZ). The Northside Achievement Zone in Minneapolis is funded through a grant from the federal Promise Neighborhoods initiative, enacted by the Obama Administration to help more communities dramatically improve the academic success for low-income children by adopting HCZ-like strategies. The East Durham Children’s Initiative in North Carolina is entirely privately funded so far (Weiss 2016e).

In both Kalamazoo, Michigan, and Orlando, Florida, pledges of “Promise” college scholarships have evolved into broader whole-child efforts (Alvarez 2015; Miller-Adams 2015).

Districts also take different approaches based on density. New York City—home to dozens of full-service community schools supported by the Children’s Aid Society and rapidly expanding to more—and Boston—home to the City Connects initiative—leverage a broad range of their respective cities’ arts and cultural offerings, along with health and nutrition and other social services (Weiss 2016g, 2016h). Cultural offerings to supplement other well-rounded services are also part of the full-service community schools district initiative in Vancouver, Washington. In contrast, Partners for Education, which serves the isolated region surrounding Berea College in Kentucky, was the first rural organization to receive a Promise Neighborhood grant and, thus, is a pioneer in exploring how well the model works outside the urban context (Berea College 2013).

What do whole-child initiatives do?

The sections below describe commonalities across these different approaches in terms of investments in children’s earliest years (before school starts), building on these investments throughout children’s K–12 years (both in and out of school), and the gains students and schools enjoy as a result of those investments. 27

How the case study districts invest in early childhood care and education

In keeping with their whole-child approaches to education policy and practice, every one of the 12 districts highlighted as a BBA case study has made investments in early childhood care and education, many of them substantial. These districts’ efforts begin long before children enter school and go beyond pre-K offerings to equip parents in the effort to ensure their children’s readiness for school.

One-on-one engagement with new parents

Investing in babies by engaging parents can include providing new parents with key information about child development and how to keep children healthy and safe. In Joplin, Missouri, Bright Futures Joplin partners with two of the area’s hospitals to deliver new baby “kits” with child development and early literacy information and is trying to raise funds to sustain the project long term and to expand it to reach every new parent (Weiss 2016a). In Vancouver, Washington, 6,000 “literacy packets” are delivered annually to families with children up to age five, providing child-development activities and lessons that families can complete at home (Weiss 2016b).

The districts leverage partnerships to connect parents with a range of school and community resources that support children from birth through kindergarten entry. In Eastern Kentucky, the whole-child program called Partners for Education works with Community Early Childhood Councils to host events such as Week of the Young Child, the Dolly Parton Imagination Library, and Kindergarten Transition Programs (Weiss 2016c). In Montgomery County, Maryland, “Judy Centers”—early child care and family education centers—leverage partnerships with social service agencies and local community nonprofits to increase parents’ access to mental health, nutrition, and other key services (Maryland State Department of Education 2017).

Educating and engaging parents early helps prepare children for school both academically and more broadly for healthy development. Those are the twin goals of the Minneapolis Northside Achievement Zone (NAZ), where currently only one in four preschoolers in the zone is ready for kindergarten based on standardized tests. To improve those odds, the zone has a team of “NAZ Navigators” who work with families to set and track progress toward goals in early childhood and to link this area of family support to goals in academics, housing, career and finance, and behavioral health (Weiss 2016d).

Parenting classes

Parents are children’s first and most important teachers. Like the one-on-one strategies described above, classes for parents provide information on child development, early literacy, health, and constructive disciplinary practices, and offer more specific guidance tailored to specific parents’ needs. Almost every district studied provides new-parent classes. The 1-2-3 Grow and Learn program is a weekly 90-minute literacy-rich program for young children and their parents offered at 12 elementary schools in high-poverty Vancouver neighborhoods. It lays the foundations for school readiness through social and education experiences. In addition, the district’s Family and Community Resource Centers offer parent workshops, groups, and courses to help parents support their children’s learning, while empowerment and skill-enhancement programs—such as job preparation, housing assistance, and parent leadership advisory groups—strengthen parents’ basic skills. Family Academy classes in the North Minneapolis Northside Achievement Zone include “College Bound Babies” (for parents of children up to three years old), which teaches early literacy, numeracy, and positive discipline skills, and “Foundations,” which empowers parents to feel confident talking with their children’s teachers and advocating for their children and their children’s schools.

In many cases, districts employ a combination of one-on-one and group supports, along the lines of Early Head Start. 28 The East Durham Children’s Initiative, a private program modeled loosely after the Harlem Children’s Zone, includes Durham Connects, a home visiting program that supports zone families with children up to age 3 and is followed by weekly or biweekly in-home parent education and support provided by two nonprofit social service providers, Healthy Families Durham and Jumpstart (Weiss 2016e). In Montgomery County, Maryland, family social workers collaborate with classroom teachers to help them develop Family Partnership Agreements, which are based on the strengths, needs, and personal goals of each family. A social worker–led team follows up by phone and with visits. In two of the district’s highest-poverty schools, these supports are complemented by early child care and family education centers (Judy Centers), which provide comprehensive early childhood education and support to children from birth to age five and their families (Marietta 2010).

Big investments in prekindergarten programs

Almost every state in the country now invests at least minimally in pre-K programs for disadvantaged children, and a growing share of states make these programs widely available. 29 Most of the districts we studied, however, have gone far beyond state programs through one or more strategies and funding mechanisms.

A few of these districts benefit from high-quality state pre-K programs that serve a large share of children, freeing the districts to invest in other aspects of early childhood enrichment. The Partners for Education initiative based in Berea, Kentucky, leverages the state pre-K program, which serves all three- and four-year olds who are either low-income or have other risk factors. This enables Partners for Education to use Promise Neighborhood grant funds to place early childhood specialists in pre-K classrooms throughout the four-county region (the region is a Promise Neighborhood region, which means that federal funds are available for a variety of education- and health-related investments). The specialists also provide coaching, professional development, and support for Head Start classrooms, as well as in-home tutoring over the summer.

In East Durham, North Carolina, strong state early education programs are supplemented by partner-led low-cost half-day preschool and a summer kindergarten readiness program, and home visits by parent advocates provide a range of supports, such as connections to state pre-K. In Kalamazoo, Michigan, the Pre-Kindergarten Early Education Program (PEEP) offers half- or full-day pre-K classes in elementary schools for four-year-olds at or below 250 percent of the federal poverty level, per state law, but it adds transportation and meals for those children. PEEP also works with other programs such as Head Start to provide families who are ineligible for PEEP with other options for low- or no-cost quality early education (KPS 2017).

Other districts with less comprehensive state support use federal resources to expand local options. For example, Vancouver draws on both state and federally funded early learning programs to provide pre-K in seven schools, along with district-supported programs for children in Title I schools. As of fall 2015, Vancouver’s new early learning center serves up to 100 additional children or more, with hot meals and playground space from an adjacent elementary school. Montgomery County also enhances state and federal programs with district-level investments: it provides the same literacy-rich curriculum in its Head Start classrooms as in district pre-K classrooms. And Montgomery County uses a blend of federal Title I and Head Start dollars to offer full-day Head Start in 18 of the poorest schools, serving 460 children (Marietta 2010). The Northside Achievement Zone in north Minneapolis uses federal Race to the Top Early Learning Fund money for scholarships for three- and four-year-olds to attend high-quality pre-K, serving 127 children in 2012–2013 and 156 in 2013–2014.

Local programs can also fill in where state programs are weak. Austin, Texas, uses local funds to provide enriching, hands-on full-day programs for the four-year-olds who would otherwise participate in lower-quality half-day state programs. Austin also provides a half-day program for three-year-olds who aren’t served by the state. Families who qualify for both state pre-K and Head Start also receive nutrition, health, and other services (AISD 2017).

Pea Ridge is another community using local resources to supplant state resources. A lack of available seats for children who are eligible for the state’s high-quality Arkansas Better Chance (ABC) pre-K program prompted Pea Ridge to seek a grant to open its own program, which serves 40 children: 20 at-risk children, who receive tuition scholarships, and 20 others whose parents can pay tuition (Weiss 2016f). Missouri’s pre-K program also has too few slots, so Bright Futures Joplin is building a new early childhood learning center that will be funded jointly by the district and the state.

Strengthening the transition to kindergarten

Featured districts also build on pre-K gains and help narrow school-readiness gaps with such programs as full-day kindergarten. Montgomery County Public Schools first started full-day kindergarten in “red zone schools,” those deemed to be most affected by high rates of student poverty, in 2000. Full-day kindergarten has since expanded to every school in the district (Marietta 2010). And Vancouver offers Kindergarten Jump Start, a school readiness program, at all 21 elementary schools, and full-day kindergarten; both programs seek to enhance the transition from pre-K into formal schooling.

Other investments in young children and their families

In addition to the above range of supports for infants, toddlers, and preschoolers and their parents, several of the districts studied by BBA have made additional investments in young children and their families. The Community Storywalk in Clay County, Kentucky, and the Born Learning Trail in Joplin, Missouri, provide opportunities for parents and paid caregivers to learn with their children in a hands-on way through outdoor and physical activities. In Eastern Kentucky, Partners for Education’s Promise Neighborhood grant supports work by national nonprofit Save the Children to improve the health and education outcomes of the region’s children through a literacy program that provides kids ages 5–12 with books and tools to develop strong reading skills. The Promise Neighborhood grant also allows Partners for Education to offer the Children’s Healthy Choices program, which provides healthy snacks and 30 minutes of daily physical activity for children in districts across Eastern Kentucky.

Joplin’s Little Blue Bookshelf program gives age-appropriate books to those children whose families cannot afford them, making the goal of 1,000 hours of reading by kindergarten a viable reality for every child. And the city’s Lend & Learn Libraries provide stimulating toys and socialization time for young children and their parents.

How the school districts invest in K–12 strategies to sustain and boost their early childhood investments

The whole-child approaches these communities embrace for children from birth to five years old continue as those children transition to kindergarten and through elementary, middle, and high school. This represents a sharp difference from most other districts, which focus heavily on narrow academic factors and assessments and thus neglect characteristics emphasized in pre-K, such as building strong teacher–student relationships and attending to the full range of children’s assets and needs. As these examples illustrate, students continue to benefit from a more comprehensive approach to education and there is an array of strategies school districts can use to deliver that comprehensive approach.

Enriching K–12 curricula and activities to sustain pre-K’s whole-child emphasis

A broad set of investments and activities can help sustain pre-K’s whole-child approach, including enhancing classroom experiences, aligning classroom lessons with out-of-school activities that expand children’s worldviews, and using targeted strategies to improve students’ readiness for college, careers, and civic engagement.

Schools that ensure hands-on learning both in and out of the classroom make the most of this opportunity. Joplin and Pea Ridge students and their teachers enjoy service learning projects that are a core component of the Bright Futures strategy. These range from kindergartners organizing coat drives and canned food drives for their neighbors to high school students designing and implementing water research projects and reporting on the health and safety of Joplin’s water supply to the city’s water management agency. In East Durham, partnerships with community agencies and nonprofits enable clubs, field trips to museums, and other enrichment activities.

After-school and summer programs help students build on what they learned during the school year, broaden students’ worldviews and skills, and reduce summer learning loss. In most of the districts studied, schools partner with organizations such as the YMCA, Boys and Girls Clubs, Boy Scouts, and Girl Scouts to provide out-of-school enrichment programs that range from organized sports and help with homework to math and book clubs, theater, and robotics. In addition to boosting student engagement, some focus in particular on academic and college preparatory help, and many also provide snacks or even full meals. Summer camps in Boston and East Durham and book deliveries and clubs in Pea Ridge and Eastern Kentucky—where online options help bridge long distances in rural areas—keep students reading, engaged, and on track for fall classes.

In several districts, the focus on nurturing not only students’ academic skills but also their social and emotional skills strengthens the transition to kindergarten and development throughout the K–12 years. Vancouver’s schools teach and model social and emotional learning in classrooms as part of the district’s work to improve school climate and track student data on engagement and mental health. Under City Connects—the whole-child collaboration among Boston College, Boston Public Schools, and community agencies—school coordinators meet at the start of the year with teachers to discuss the particular strengths and needs of each student and develop plans to support teachers with academic and enrichment activities and meet student needs with small-group sessions on healthy eating and dealing with bullies, referrals to mental health providers, and a range of other supports (Weiss 2016g).

Two districts have made social and emotional learning a particularly high priority. Austin is one of eight districts working with the Collaborative for Academic, Social, and Emotional Learning (CASEL) to comprehensively embed social and emotional learning in teacher training, teacher standards, curricula, and metrics for assessing student and school progress (CASEL 2017). In Montgomery County, former superintendent Joshua Starr drew on the Common Core’s emphasis on problem-solving and critical thinking to lead the design of a new curriculum and classroom practices that nurture social and emotional skills. These are complemented by enhanced support for teachers to nurture social and emotional learning in daily classroom practice, by standards-based report cards that track key social and emotional skills, and by constructive disciplinary policies that reengage students and build their soft skills instead of punishing them for infractions. 30

Several of the districts focus in particular on helping students—many of whom will be the first in their families to go to college—prepare for and make that leap. Strategies include middle-to-high-school transition programs in Joplin and Vancouver and clubs and specialized courses that advance students’ social and organizational skills in Vancouver and Montgomery County. In East Durham, three initiatives (Communities in Schools Durham, Student U, and Citizens in Schools) support youth who are preparing for graduation. They offer site-based mentoring from current undergraduates. Middle and high school students in the North Minneapolis Northside Achievement Zone receive similar assistance. And Vancouver’s GRADS Teen Parent program helps teen parents stay in school, graduate, and be more effective parents. De-tracking, an intentional decision to not separate students who are achieving at different levels into different classrooms or types of courses, which is the norm in Austin and in some Montgomery County high schools, helps ensure that college preparatory classes serve students of all income levels rather than just wealthier, nonminority students. 31

College readiness is also a high priority for many Bright Futures districts. In Joplin, programs such as Operation College Bound enhance students’ understanding of and access to postsecondary education, complementing initiatives that help students navigate transitions to higher education and other sensitive periods of their academic lives. And in Pea Ridge, specialized high schools such as the Manufacturing and Business Academy and Pea Ridge Academy provide targeted support for students who want to go straight to jobs and careers or need special academic supports.

Mentoring and tutoring to get and keep students engaged

In the case study districts, the whole-child approach includes understanding the critical importance of one-on-one relationships with caring adults who support children’s academic and broader needs. Strategies can be as simple as the car and bus “buddies” who greet children in Pea Ridge each morning as they arrive at school, or as intensive as the volunteer “lunch buddies” who meet regularly with Joplin and Pea Ridge students to eat with them, talk about their days, and offer guidance. Northside Achievement Zone in North Minneapolis partners with Big Brothers Big Sisters to connect students with mentors, and over 500 volunteer mentors in Vancouver, Washington, support students in Family and Community Resource Centers.

These relationships are key to efforts in large urban districts and remote rural ones. The Children’s Aid Society has partnered with the New York City Department of Education to integrate a strong school curriculum with out-of-school enrichment programming, as well as provide child and family support services designed to remove barriers to students’ learning (Weiss 2016h). Children’s Aid community schools offer both tutoring and mentoring among their after-school options, as do Boston’s City Connects schools. In Eastern Kentucky, to bridge the long distances between one school and community and another, mentors use Skype to connect with eighth- and ninth-graders in Promise Neighborhood area schools.

Supports for student health and family wellness as a tool for sustaining early gains

Several of the districts studied have established health clinics in some or all of their schools, including Montgomery County, Vancouver, and New York City. In some other districts, such as Austin, school coordinators can arrange for mobile clinics to come to schools. These clinics provide basic preventive care through immunizations and check-ups, along with prescriptions and other care for sick children, physical and mental health screenings, follow-up counseling, mental health care, and even crisis intervention when needed.

Nutrition is another critical factor that affects physical and mental health and thus learning. In East Durham, Back Pack Buddies and summer lunch programs prevent hunger and keep kids nourished. Food and clothing pantries plus social media outreach in Pea Ridge and Joplin enable counselors and teachers to meet targeted immediate needs so students can focus and learn. Montgomery County has expanded its breakfast-in-the-classroom program to serve all students in a growing share of schools (MCPS 2017).

Many of these districts look beyond meeting students’ basic health and nutrition needs to advancing their and their families’ wellness and strengthening their ties to the community. Vancouver’s GoReady! back-to-school festivals provide backpacks, school supplies, shoes and socks, immunizations and dental screenings, and even haircuts, plus resources from community partners . In Eastern Kentucky, physical and mental health supports provided through state-supported Family Resource and Youth Service Centers are complemented by school–community collaborative activities through a run/walk club, a summer fitness program, a Jump Start program, and gardening and food preservation activities. And the East Durham Children’s Initiative runs a Healthy Living Initiative that refers families to nutrition counseling programs, Zumba classes, cooking demonstrations, and walking groups; it also distributes children’s bicycles and partners with local farmers markets to provide families with fresh produce.

Though research has long affirmed the importance of parental engagement, many schools struggle to meaningfully engage parents. The case study districts show how it can be done. In the rural regions around Berea, Kentucky, where physical distance makes engagement difficult, Partners for Education’s Families and Schools Together project convenes parents, school staff, and local agency professionals to help parents build social networks. In the North Minneapolis Northside Achievement Zone (NAZ), a high-poverty heavily minority area, regular one-on-one meetings between parents and “connectors”—specialized social workers who grew up in the area, are familiar with its challenges, and are a core component of the NAZ strategy—provide opportunities to conduct family needs assessments and provide referrals to relevant services. These regular meetings lead to deeper parental engagement in their children’s schools.

And full-service community schools such as those in Vancouver and New York City specialize in parent outreach and engagement. Community schools in these districts draw on parental input to shape school policies and practices and provide parents with an opportunity to meet one another. For example, a “parents’ coffee room” in a New York City school with a large Dominican population evolved from simply providing a space for parents to hang out after student drop-off to a center for parent-led workshops, parent–student collaborative plays, and more.

Other targeted supports provide added help for the most vulnerable students and their families. In Vancouver, for example, student advocates conduct home visits to parents of kindergartners and first-graders who are at risk of chronic absenteeism. In these visits, the advocates emphasize the importance of attendance and brainstorm with parents ways to reduce specific barriers to attendance. Complementary in-school efforts reward strong attendance. High-risk Montgomery County Public Schools students benefit from an unusual, but very effective, system of targeted support. Specifically, the districts’ funding system redistributes money from wealthier schools to higher-poverty schools, enabling the latter to provide smaller classrooms, more individualized attention, and more specialists in English language learning, special education, and other areas (Elmore, Thomas, and Clayton 2006).

How academic gains, including smaller achievement gaps, indicate that the investments are paying off

Providing children from birth through 12th grade and their families with targeted supports both within and outside of school has enabled these communities to make progress toward a range of goals. First, compared with students in peer districts, these districts’ students tend to have better outcomes on traditional measures of academic achievement such as test scores and graduation rates. Second and just as, if not more, important, these districts have improved students’ kindergarten readiness, engagement, and health and well-being, and helped the students be better prepared for college, careers, and civic engagement. This is true in large part due to these districts’ intentional bucking of a growing trend of diverging practices in which students in high-poverty schools are subject to narrow academic drilling while students in wealthy schools benefit from a broader set of activities and learning experiences beyond a narrow focus on preparing for standardized tests. These districts ensure enrichment for all students, regardless of socioeconomic status. Finally, in contrast with the national trend in recent decades of rapidly growing achievement gaps between wealthy and poor students, these districts are also narrowing race- and income-based achievement gaps: while all students are gaining ground, those who started off behind tend to see the largest gains.

Most of the data presented in this section do not come from experimental studies; with a few exceptions (which are noted in the case studies), they rely on nonexperimental comparisons with a similar nontreatment group, such as other low-income children in the district or other high-poverty districts in the state. However, they are gathered from official district, state, or federal resources in all cases, except for the minority of cases in which such data are not publicly available. Perhaps most importantly, in contrast with many other programs that have reported substantially improved outcomes for very vulnerable groups of students, these programs do not cherry-pick students to get these results. Rather, these initiatives serve all students in the enrollment area for a school, a cluster of schools, or, in many cases, an entire district; as described above, they are serving some of the nation’s most vulnerable students and their families. 32 Moreover, many of these efforts are, for lack of a better term, “turnarounds.” That is, students in an existing system that is considered to be failing are offered a new approach in the same school building, making the large gains reported particularly striking given the notable lack of similar progress from much-larger-scale, more publicized attempts at employing other turnaround strategies. 33

Establishing more expansive goals and implementing ways to track progress toward those goals also offers timely guidance, given that the Every Student Succeeds Act (ESSA) asks states, districts, and schools to do just that. These districts have not only set broader goals, they are demonstrating real progress toward achieving these goals. Because of their success, many now serve as role models for other districts or entire regions, and a few are beginning to influence state policy as well.

Higher rates of kindergarten readiness predict school success

Some of the kindergarten readiness efforts described above have translated into improved readiness to learn and, thus, greater odds of success in kindergarten and throughout the K–12 years. In Eastern Kentucky, East Durham, and Minneapolis, children who participated in early learning programs significantly increased their rates of kindergarten readiness across a range of metrics and developmental domains. A study of Montgomery County Public Schools found much larger gains in reading for children in the full-day Head Start program than for children in the half-day program, with full-day students more than doubling their reading scores over the year and especially pronounced gains for the most vulnerable students: Hispanics and English language learners (Marietta 2010).

Rising test scores and narrowing gaps in core academic subjects are an important sign of sustained early gains

While only one of many indicators, rising test scores and narrowing gaps in core academic subjects are an important sign that schools in case study districts have sustained and enhanced early gains. Despite serving a higher percentage of low-income, black, Hispanic, and English language learner students than the district average, Austin’s Alliance Schools—schools in which community organizers have worked to empower parents in conjunction with teacher advocacy efforts—saw substantial gains in scores on the Texas Assessment of Academic Skills, the state’s main standardized test, in the three years after parent-organizing efforts began. Increases varied from four points to 15–19 points, with the latter increases occurring in schools with the highest levels of parental engagement (Henderson 2010). Subsequent rollout of social and emotional learning in district schools (some of which were also Alliance schools) produced gains in the share of students deemed proficient on the State of Texas Assessment of Academic Readiness (STAAR, the next-generation state assessments) in the years following that rollout, with students in the first set of schools with social and emotional learning programs scoring higher on state math and reading exams than those in later school cohorts. The small group of Minneapolis Northside Achievement Zone students who were tested increased their proficiency on the Minnesota Comprehensive Assessments (MCA) exam, with the share scoring as proficient rising from 14 percent in the 2012–2013 academic year to 22 percent in 2013–2014. 34 Students who had enrolled in the Northside Achievement Zone in 2013 had larger gains than those who enrolled in 2014, and, overall the largest proficiency gains were among first- and second-graders, with the smallest gains in middle schools.

Despite serving a much poorer and socially and economically isolated student body than in state schools overall, the Eastern Kentucky schools served by Partners for Education have seen substantially higher increases in test scores: from 2012 to 2015, math test scores in the Promise Neighborhood region rose 7.0 percentage points compared with 4.4 percentage points across the state, and reading scores rose 7.3 percentage points, compared with 5.8 percentage points statewide.

An independent study of middle school students who participated in the after-school programs run by Children’s Aid Society community schools in New York City had bigger gains in math and reading test scores than peers who did not participate. They also had higher relative increases in school attendance and in teacher-reported “motivation to learn.” And while the Children’s Aid Society did not make early childhood education investments a core component of its strategy, its Zero-to-Five program, which connects the federal Early Head Start and Head Start programs, produced relative test score gains among participants. Specifically, a study found that participants outperformed their peers 97 percent of the time on third-, fourth-, and fifth-grade standardized tests in math and reading, demonstrating a significant long-term positive effect (Caspe and Lorenzo Kennedy 2014).

Increases (or lack of decreases) in reading scores over the summer months (between the end of the school year and the start of the following year) can be an especially important indicator of sustainable academic achievement, since low-income students tend to lose substantial ground when they are out of school for the summer. Students who attended the North Minneapolis Northside Achievement Zone’s extended learning summer programs increased their reading test scores between the end of one school year and the beginning of the next, a period when scores normally decrease. And an evaluation of students who attended the East Durham Children’s Initiative’s summer camp in the summer of 2014 found that they lost no ground in literacy over those months.

Case study districts with more mature initiatives and those offering higher or more intensive doses of whole-child interventions are producing particularly large academic gains. Students enrolled in City Connects elementary schools in Boston score significantly higher on tests of both academic and noncognitive skills in elementary and secondary school, with the highest-risk students, such as English language learners, showing especially large gains. Scores of City Connects elementary school students on the Stanford Achievement Test version 9 increased between one-fourth and one-half a standard deviation greater than scores of their non–City Connects peers. And graduates of City Connects secondary schools are more likely to attend one of Boston’s three most selective public high schools.

Better student attendance and engagement are also predictors of academic gains

Chronic absenteeism depresses achievement, particularly among low-income students. A 2009 study found that New York City Children’s Aid Society’s community schools had “far higher” attendance than peer schools, and that schools with health centers tended to have higher attendance than those without health centers (Clark et al. 2009). Students attending City Connects high schools in Boston have significantly lower rates of chronic absenteeism than their peers (Boston College Center for Optimized Student Support 2012). In Joplin, Missouri, attendance rates among high school students increased 3.7 percentage points, rising from 91.3 percent in 2008 to 95.0 percent in 2012; black and Hispanic students closed gaps with their white peers over that period. At the same time, reportable disciplinary incidents—which keep students out of school and are found to drive at-risk students to disengage—dropped by over 1,000, from 3,648 in 2008 to 2,376 in 2012. 35

Every infant and toddler in East Durham whose family participated in the Healthy Families Durham home visiting program is up to date on immunizations; this helps at-risk children avoid missing school due to illness. In Pea Ridge, collaboration with one of the city’s doctors enabled the district to provide physical exams for high school students who would otherwise go without them. This not only improved their health but enabled them to participate in the kinds of extracurricular sports activities that boost student engagement. And City Connects’ practice of helping families draw on Medicaid coverage and of referring eligible students to insurance-eligible providers increases students’ access to both physical and mental health care. Given extensive evidence linking reduced absenteeism and improved physical and mental health to academic gains, these initiatives’ records of boosting both attendance and health represent another pathway to student success. 36

Increases in advanced coursework and completion of associated exams suggest improved college and career readiness

Because most of the initiatives studied have been in place for less than 10 years, and a few for five or fewer, there is less evidence of their impact on high school graduation and college enrollment. Nonetheless, the degree to which low-income and minority students in these districts perform better and have seen greater gains on these key indicators than their peers in comparable districts or across the state highlights the promise of comprehensive education approaches and, in some instances, their capacity to sustain and even boost children’s early gains.

Parent-organizing in Austin helped establish a program to get more low-income and minority middle school students into rigorous science and math programs, enabling them to successfully compete for slots in the prestigious LBJ High School Science Academy. From the 2007–2008 to the 2014–2015 academic year, the number of Kalamazoo Public School students taking Advanced Placement (AP) courses more than doubled, with low-income and African American students experiencing the largest absolute gains in participation and Hispanic students experiencing the largest percentage gains. Black and low-income students roughly quadrupled their participation in such courses; 263 black students and 193 low-income students took AP classes during the 2014–2015 academic year, up from 63 and 53 respectively in 2007–2008 (Miller-Adams 2015). Over the same period, the number of Hispanic students taking AP courses increased by a magnitude of 10—from just 8 to 78. And in Vancouver, which also made socioeconomic diversity of students in advanced courses a priority, enrollment in AP courses rose by 67 percent overall from 2007–2008 to 2013 –2014, and nearly three times as fast, by almost 200 percent, among low-income students.

Higher graduation rates and increasing college attendance of disadvantaged students are another measure of success of comprehensive strategies

In the early 2000s, the graduation rate at Austin’s Reagan High School fell below 50 percent and enrollment dropped to just 600 students. By 2015, with the benefit of a community schools strategy, the school was serving more than 1,200 students and had a graduation rate of 85 percent.

In the first six years of Bright Futures, Joplin’s graduation rate rose from 73 to 87 percent; from 2012 to 2015 it rose 13 percentage points, versus just 5 percentage points across the state as a whole. At the same time, the cohort dropout rate fell from 6.4 percent to 2.8 percent, with the dropout rate for black students falling slightly more. And in Kalamazoo, incentives to finish high school have proven to be powerful tools for disadvantaged students when combined with mentoring, tutoring, and after-school options. The district’s graduation rate rose from 64 percent in 2009 to 69 percent in 2014, with “five-year cohort graduation rates consistently higher than four-year rates, suggesting that some students may be opting to stay in school an extra year (or even just for the summer) to complete the credits necessary to get a high school diploma” (Miller-Adams 2015, 67). Moreover, African American girls in Kalamazoo graduate at higher rates than their peers across the state, and 85 percent of those graduates go to college.

Initiatives that have had time to mature have made particularly large gains. Montgomery County’s Linkages to Learning initiative began in 1993 and it substantially expanded its pre-K program around a decade later; a county policy responsible for improved racial integration has been in place even longer, since the early 1970s. Hispanic, low-income, and African American students in Montgomery County Public Schools are much more likely than their counterparts across the state to graduate from high school—80.0 vs. 77.5 percent, 81.0 vs. 77.8 percent, and 86.4 versus 80.5 percent, respectively. And from 2011 to 2014, a period when the share of students in poverty and the share of minority students rose in the district, overall graduation rates rose 2.9 percentage points, from 86.8 to 89.7 percent. There were much larger gains for Hispanic and black students, whose graduation rates rose (respectively) by 4.7 percentage points (from 75.3 to 80.0 percent) and 5.1 percentage points (from 81.3 to 86.4 percent), thus narrowing their gaps with their white peers by 3.4 and 3.8 percentage points, respectively (MCPS 2015). Participation in Boston’s City Connects program, which began in 2001, cuts a student’s odds of dropping out of high school nearly in half: 8.0 percent versus 15.2 percent for comparison students (Boston College Center for Optimized Student Support 2014). In Vancouver, the four-year graduation rate rose from 64 percent in 2010 to almost 80 percent in 2013, and the five-year rate rose from 69 percent in 2010 to over 80 percent in 2013. Vancouver’s Hispanic students had five-year graduation rate gains of over 15 percentage points.

Strong parent and community engagement is another sign of progress

The comprehensive, whole-child, whole-community approaches in the featured school districts have built strong school–community partnerships. Two indicators of the strength of the partnerships are the levels of parent and community engagement. In Joplin, 194 more adults are now serving as mentors and tutors than five years ago. And the American Association of School Administrators, National School Public Relations Association, and Blackboard Connected selected Vancouver Public Schools Superintendent Steve Webb and Chief of Staff Tom Hagley for their 2011 Leadership through Communication Award for their successful efforts to increase family engagement in high-poverty VPS schools.

Parental engagement boosts student achievement both directly and through other improvements to families’ situations. As they work actively with their “connectors,” Northside Achievement Zone parents in North Minneapolis become more likely to make academics a priority, to engage with their children’s schools, and to be focused on sending their children to college. The support also helps more families connect with stable housing, substantially reducing the number of times that some vulnerable families move. In 2014–2015, up to 300 Austin families benefited from help with legal, employment, health, and housing issues at the family resource center, which also provides classes for parents, including English language learning classes. And Montgomery County Public Schools social workers who specialize in early childhood education make an average of 200 home visits, 1,000 phone contacts, and 300 direct contacts with parents at school or conferences each month. These lead to roughly 1,000 monthly referrals to community services—many of them emergency interventions dealing with food, clothing, and housing—that help families meet their children’s basic needs and, thus, support their children’s education (Marietta 2010).

In some cases, engagement enhances school leadership. Through access to supports such as social services and adult education, parents of students in New York’s Children’s Aid Society community schools got more involved in their children’s schools, took more responsibility for their children’s schoolwork, reported feeling more welcome within the schools, and were observed to be a greater presence in the community schools than in comparison schools. And over 2,000 Kentucky parents have undergone training at the Berea Commonwealth Institute for Parent Leadership since its creation in 1997. Many of these parents have gone on to join school boards, serve on school councils, and engage in day-to-day educational advocacy.

Expansion of these initiatives shows that other districts, and even state policymakers, consider them successful

After City Connects succeeded in improving student achievement in over a dozen of Boston’s highest-poverty schools, the initiative caught the attention of state policymakers, who recruited City Connects to help turn around schools in Springfield, home to another large high-poverty urban district in Massachusetts. Aided by federal School Improvement Grant funds, City Connects has operated in Springfield since 2010, expanding from six to 13 schools in its first four years there. In New York City, the Children’s Aid Society played a central role in Mayor Bill de Blasio’s 2016 decision to employ a community schools strategy to turn around 100 of the city’s most struggling schools. And in both Vancouver and Austin, district leaders have led advocacy efforts to bring community schools to other communities in the region and to support the introduction of state-level legislation to enhance the work.

Bright Futures began in Joplin, Missouri, in 2009 but is now a national organization. Bright Futures USA has 50 affiliates in eight states, many of which—such as Pea Ridge—are just two or three years old. The newest affiliate, in Fairbanks, Alaska, has just been made official. In Virginia, Dave Sovine, superintendent of a second-year affiliate, Frederick County Public Schools, is reaching out to several of his counterparts across the region to create the first regional Bright Futures initiative (Gizriel 2016). If established, this would allow for the kind of cross-district collaboration identified by Bright Futures founder C.J. Huff as critical to breaking down the silos created by arbitrary boundaries that reflect political preferences rather than children’s daily realities. 37

As this report demonstrates, very large social-class-based gaps in academic performance exist and have persisted across the two most recently studied cohorts of students starting kindergarten. The estimated gap between children in the top fifth and the bottom fifth of the SES distribution is over a standard deviation in both reading and math in 2010 (unadjusted performance gaps are 1.17 and 1.25 sd respectively). Gaps in noncognitive skills such as self-control and approaches to learning—which are critical not only as foundations for academic achievement but also more broadly for children’s healthy development—are about half as large (about 0.4 sd in self-control, and slightly over 0.5 sd in approaches to learning in 2010).

Another important finding from our study is that gaps were not, on average, sensitive to the set of changes that may have occurred between 1998 and 2010: gaps across both types of skills are virtually unchanged compared with the prior generation of students—those who entered school in 1998. The only cognitive gap that changed substantially was in reading skills, which increased by about a tenth of a standard deviation. The gaps by SES in mathematics, in approaches to learning as reported by parents, and in self-control as reported by teachers did not change significantly. And relative gaps in approaches to learning as reported by teachers and in self-control as reported by parents shrank between 1998 and 2010, by about a tenth of a standard deviation. 38

We also find that, while taking into account children’s personal and family characteristics, parental activities, and other factors reduces the gaps somewhat, it does not come close to eliminating them. This means that there is a substantial set of SES-related factors that are not captured by the traditional covariates used in this study but that are important to understanding how and why gaps develop. Moreover, the capacity for these other factors—child and family characteristics, early education investments, and expectations—to narrow gaps has decreased over time. This suggests that, while such activities as parental time spent with children and center-based pre-K programs cushion the negative consequences of growing up in a low-social-class context, they can do only so much, and that the overall toxicity of lacking resources and supports is increasingly hard to compensate for. The resistance of gaps to these controls should thus be a matter of real concern for researchers and policymakers.

These troubling trends point to critical implications for policy and for our society: clearly, we are failing to provide the foundational experiences and opportunities that all children need to succeed in school and thrive in life. The failure to narrow gaps between 1998 and 2010 suggests, too, that investments in pre-K programs and other early education and economic supports were insufficient to counter rising rates of poverty and its increasing concentration in neighborhoods where black and Hispanic children tend to live and learn.

But there is also good news. The case study review in the previous section of this report explores district-level strategies to address these gaps, strategies that are being implemented in diverse communities across the country. The most effective ones begin very early in children’s lives and are sustained throughout their K–12 years and beyond. The communities studied all employ comprehensive educational approaches that align enriching school strategies with a range of supports for children and their families. Their implementation is often guided by holistic data and, to the extent possible, this report provides a summary, as well, of student outcomes, using both traditional academic measures and a broad range of other measures.

These findings also point to further research questions that need to be addressed, including why gaps changed or did not change, for whom they changed (or did not change), and what is the absolute change in children’s skills over time. 39

Parents are doing what they need to do, and a growing number of communities are, too, but as a society, we are still falling far short

Over the period studied, parents across all social class groups became more involved in their young children’s early education and development, with increases in involvement being especially pronounced among low-SES parents. Parents were more likely in 2010 than in 1998 to read regularly to their children; to sing to them; to play games with them; and to enroll them in center-based pre-K programs. Parents in 2010 also had significantly higher expectations for their children’s educational attainment, and mothers themselves were more highly educated—both factors that are associated with higher achievement for those children. In other words, parents’ actions show that they are doing more of what the brain science indicates they need to do, which either suggests that information about children’s needs during those years is more widely disseminated than it was for the prior cohort we studied, or that parenting styles have changed in a way that benefits the development in the early years.

And, as the case studies indicate, the number of communities that have embraced systems of comprehensive enrichment and supports (“Broader, Bolder Approaches to Education”) is growing. As these communities have shown, such comprehensive education policies are feasible; embedded in these policies is an understanding that children’s development involves nurturing a variety of competencies throughout the stages of development, that there are many individuals participating in these processes, and that coordinated efforts by various stakeholders are needed to put these processes to work. Key principles that span across the case studies include very early interventions and supports, parental engagement and education, pre-K, kindergarten transitions, whole-child approaches to curricula, and wraparound supports that are sustained through the K–12 years. Given the significant need for more such strategies, it is important to understand the factors that drove their enactment in a diverse set of communities, and to continue to monitor both the challenges these communities (and others like them) encounter and the outcomes/benefits of the initiatives.

However, despite the abundance of child development information available to researchers and parents—about the serious impacts of child poverty, about what works to counter those effects, about the importance of the first years of life for children, and about the value of education—our data indicate insufficient policy response at all levels of government. Pre-K programs have expanded incrementally and unevenly, with both access and quality still wildly disparate across states and overall availability severely insufficient. There is a dearth of home visiting programs and of quality child care (Bivens et al. 2016). Child poverty has increased (see Proctor, Semega, and Kollar 2016 for recent trends in child poverty rates). And the schools these children enter face increasing economic and racial segregation but with even fewer resources than they had in 1998 to deal with them (Adamson and Darling-Hammond 2012; Baker and Corcoran 2012; Carnoy and García 2017). And while a growing number of districts have embraced Broader, Bolder approaches, that number is failing to keep up with high and growing need.

In sum, it is actually positive, and somewhat impressive, that gaps by and large did not grow in the face of steadily increasing income inequality, compounded by the worst economic crisis in many decades (EPI 2012, 2013; Saez 2016). But it is disappointing and troubling that new policy investments made in the previous decade were insufficient to make even a dent in these stubborn gaps. We cannot ensure real opportunities for all our children unless we tackle the severe inequities underlying our findings. And while momentum to enact comprehensive and sustained strategies to close gaps is growing, such strategies are not being implemented nearly as quickly as children need them to be.

Next policy steps

These data on large, stubborn gaps across both traditional cognitive and noncognitive skills should guide the design of education policies at the federal, state, and local levels; the combined resources and support of government at all three levels are needed if we are to tackle these inequalities effectively. 40

Policymakers can begin by learning from the small-scale, district-level strategies presented in the review of case studies above (see the section “What are pioneering school districts doing to combat these inequities and resulting gaps?” above). Looking at these case studies, policymakers can ask: What are the key strategies these communities employed, what main components characterize these strategies, and how did these communities effectively implement the strategies? What challenges did these communities face, what was needed to overcome the challenges, and how can we shape policies that better support other communities’ abilities to respond to such challenges and, to the extent possible, avert them? The latter set of questions is particularly pertinent to issues of scalability, financing, and sustainability, all of which have posed significant challenges for the districts studied and others like them. Policymakers can further ask: What other sources or examples might we learn from? Obvious ones include other districts that employ “community schools” strategies (as Vancouver, New York City, and Austin do) and Promise Neighborhood initiatives beyond Berea/Eastern Kentucky and the Northside Achievement Zone. Bright Futures affiliates now exist in 50 districts across eight states—and the program continues to grow—offering another set of communities to look to.

Also, new opportunities under the Every Student Succeeds Act (ESSA)—from funding to expand and align early childhood education programs to broader and more supports-based educator- and school-accountability systems—provide another avenue for exploration and educational improvement. This is already the focus of states and districts across the country—as well as of education policy nonprofits and associations—and is a focus that has the potential to inspire viable larger-scale models (Cook-Harvey et al. 2016).

We must take action, in particular, in those areas of policy related to early education in which we have seen little or no progress over the past decade. These include child care: comprehensive supports that engage parents as partners in their children’s education must start early and be of high quality to prevent the emergence of gaps and provide time to close any gaps that emerge (Bivens et al. 2016, among others). Quality preschool, among the most-agreed-upon strategies to avert and narrow early gaps, continues to be much talked about but far too little invested in and far too infrequently and shoddily implemented. The advantages of preschool have been known for decades, and significant progress has been made in preschool enrollment over that time; however, preschool enrollment stagnated soon after 2000 (Barnett et al. 2017; U.S. ED 2015) and there continue to be significant inequities in access (see Table 2; García 2015) and, just as important, in quality (NIEER 2016). And the gains made through these early, whole-child-oriented supports must be sustained through children’s K–12 years, with attention to issues of funding levels and equity, racial and socioeconomic integration, and enriching opportunities in the hours after school and in the summer months.

Altogether, this report adds to the strong evidentiary base that identifies strategies to reduce the education consequences of economic inequality. It also sheds light on the need to conduct further research on the channels that drive or cushion changes in readiness. A close follow-up of these trends in the near future and of the measures adopted to really tackle inequities will not only determine what type of society we will be, but will also say a lot about what type of society we actually are. This study, affirming a growing number of other studies on these issues, points to an “American Dream” that is alive in public pronouncements but dormant and pale in reality.

About the authors

Emma García is an education economist at the Economic Policy Institute, where she specializes in the economics of education and education policy. Her areas of research include analysis of the production of education, returns to education, program evaluation, international comparative education, human development, and cost-effectiveness and cost-benefit analysis in education. Prior to joining EPI, García conducted research for the Center for Benefit-Cost Studies of Education and other research centers at Teachers College, Columbia University, and did consulting work for the National Institute for Early Education Research, MDRC, and the Inter-American Development Bank. García has a Ph.D. in economics and education from Teachers College, Columbia University.

Elaine Weiss served as the national coordinator for the Broader, Bolder Approach to Education (BBA) from 2011 to 2017, in which capacity she worked with four co-chairs, a high-level task force, and multiple coalition partners to promote a comprehensive, evidence-based set of policies to allow all children to thrive. Weiss came to BBA from the Pew Charitable Trusts, where she served as project manager for Pew’s Partnership for America’s Economic Success campaign. Weiss was previously a member of the Centers for Disease Control and Prevention’s task force on child abuse and served as volunteer counsel for clients at the Washington Legal Clinic for the Homeless. She holds a Ph.D. in public policy from the George Washington University and a J.D. from Harvard Law School.

Acknowledgments

An earlier version of this paper was prepared for “Strong Foundations: The Economic Futures of Kids and Communities,” the Federal Reserve System Community Development Research Conference, Washington, D.C., March 23–24, 2017. We appreciate the feedback we received from our discussant Richard Todd and from the audience. The authors gratefully acknowledge Rob Grunewald and Milagros Nores for their insightful comments and advice on earlier drafts of the paper. Special gratitude is expressed to Sean Reardon, for his advice and thorough guidance on the sensitivity analyses affecting the measurement of the cognitive skills and their implications for our study, and for sharing useful materials to help test our results. We thank Ben Zipperer and Yilin Pan for their advice on issues associated with multiple imputation of missing data. We are also grateful to Lora Engdahl and Krista Faries for editing this report, and to Margaret Poydock for her work preparing the tables and figures and formatting the report. Finally, we appreciate the assistance of communications staff at the Economic Policy Institute who helped to disseminate the study, especially Dan Crawford, Kayla Blado, and Elizabeth Rose.

Address correspondence to: Economic Policy Institute, 1225 Eye St. NW, Suite 600, Washington, D.C., 20005. Email: [email protected] ; [email protected] .

Figures and tables

Unadjusted cognitive and noncognitive skills gaps between high-ses and low-ses children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010.

	Gap between high-SES (fifth) and low-SES (first) quintiles in 1998	Change in gap from 1998 to 2010
Reading	1.07	0.10
Math	1.26
Self-control (by teachers)	0.39
Approaches to learning (by teachers)	0.63	-0.12
Self-control (by parents)	0.47	-0.08
Approaches to learning (by parents)	0.54

The data below can be saved or copied directly into Excel.

The data underlying the figure.

Notes: SES refers to socioeconomic status. The gaps are the baseline unadjusted standard deviation scores for high-SES children relative to low-SES children. The gap in 2010 equals the gap in 1998 plus the change in the gap from 1998 to 2010. For example, the gap in approaches to learning as reported by teachers in 2010 is 0.51 sd (0.63 – 0.12). For statistical significance of these numbers, see Tables 3 and 4, Model 1.

Source: EPI analysis of ECLS-K, kindergarten classes of 1998–1999 and 2010–2011 (National Center for Education Statistics)

Copy the code below to embed this chart on your website.

Fully adjusted cognitive and noncognitive skills gaps between high-SES and low-SES children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010

	Gap between high-SES (fifth) and low-SES (first) quintiles in 1998	Change in gap from 1998 to 2010
Reading	0.60
Math	0.61
Self-control (by teachers)	0.18
Approaches to learning (by teachers)	0.44
Self-control (by parents)	0.29
Approaches to learning (by parents)	0.13	0.11

Note: SES refers to socioeconomic status. The gaps are standard deviation scores for high-SES children relative to low-SES children after adjusting for all family and child characteristics, pre-K schooling, and enrichment activities with parents, and parental expectations for children’s educational attainment. The gap in 2010 equals the gap in 1998 plus the change in the gap from 1998 to 2010. For statistical significance of these numbers, see Tables 3 and 4, Model 4.

Unadjusted cognitive and noncognitive skills gaps between high-SES and low-SES children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010, using mother's educational attainment as a proxy for socioeconomic status

	Gap between top and bottom quintiles in 1998	Change in gap from 1998 to 2010
Reading	1.29
Math	1.46	-0.15
Self-control (by teachers)	0.32	-0.10
Approaches to learning (by teachers)	0.64	-0.24
Self-control (by parents)	0.47	-0.14
Approaches to learning (by parents)	0.66

Notes: The gaps are the baseline unadjusted standard deviation scores for high-SES children relative to low-SES children where high-SES children have mothers in the top quintile of the education distribution and low-SES children have mothers in bottom quintile of the education distribution. The gap in 2010 equals the gap in 1998 plus the change in the gap from 1998 to 2010. For statistical significance of these numbers, see Table 7, Model 1.

Unadjusted cognitive and noncognitive skills gaps between high-SES and low-SES children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010, using household income as a proxy for socioeconomic status

	Gap between top and bottom quintiles in 1998	Change in gap from 1998 to 2010
Reading	1.09	-0.13
Math	1.31	-0.23
Self-control (by teachers)	0.42
Approaches to learning (by teachers)	0.60	-0.13
Self-control (by parents)	0.44
Approaches to learning (by parents)	0.44

Notes: The gaps are the baseline unadjusted standard deviation scores for high-SES children relative to low-SES children where high-SES children are in households with incomes in the top quintile of the income distribution and low-SES children are in households with incomes in bottom quintile of the income distribution. The gap in 2010 equals the gap in 1998 plus the change in the gap from 1998 to 2010. For statistical significance of these numbers, see Table 8, Model 1.

Unadjusted cognitive and noncognitive skills gaps between high-SES and low-SES children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010, using number of books the child has in the home as a proxy for socioeconomic status

	Gap between top and bottom quintiles in 1998	Change in gap from 1998 to 2010
Reading	0.74	0.08
Math	0.97
Self-control (by teachers)	0.32
Approaches to learning (by teachers)	0.46
Self-control (by parents)	0.28
Approaches to learning (by parents)	0.58	0.09

Notes: The gaps are the baseline unadjusted standard deviation scores for high-SES children relative to low-SES children where high-SES children have a number of books in the home in the top quintile of the books-in-the-home distribution and low-SES children have a number of books in the home in the bottom quintile of the books-in-the-home distribution. The gap in 2010 equals the gap in 1998 plus the change in the gap from 1998 to 2010. For statistical significance of these numbers, see Table 9, Model 1.

Reading and math achievement gaps, and principal noncognitive skills gaps between high-SES and low-SES children at the beginning of kindergarten in 2010–2011, under unadjusted and clustered models

	Reading		Mathematics		Self-control (by teachers)		Approaches to learning (by teachers)		Self-control (by parents)		Approaches to learning (by parents)
	1 (unadjusted)	2 (clustered)	1 (unadjusted)	2 (clustered)	1 (unadjusted)	2 (clustered)	1 (unadjusted)	2 (clustered)	1 (unadjusted)	2 (clustered)	1 (unadjusted)	2 (clustered)
Gap in 2010–2011	1.169***	0.944***	1.250***	0.911***	0.386***	0.363***	0.513***	0.562***	0.391***	0.326***	0.563***	0.460***
	(0.024)	(0.036)	(0.024)	(0.034)	(0.029)	(0.041)	(0.027)	(0.041)	(0.028)	(0.041)	(0.028)	(0.044)
Controls
Demographics	No	No	No	No	No	No	No	No	No	No	No	No
Education and engagement	No	No	No	No	No	No	No	No	No	No	No	No
Parental expectations	No	No	No	No	No	No	No	No	No	No	No	No
School fixed effects	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
Observations	14,090	14,090	14,040	14,040	12,180	12,180	13,280	13,280	12,890	12,890	12,900	12,900
Adjusted R2	0.165	0.281	0.190	0.276	0.021	0.114	0.034	0.105	0.018	0.028	0.037	0.118

Note: Using the full sample. For statistical significance, *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1. The number of observations is rounded to the nearest multiple of 10. Sizes may differ from those inferred from Tables 3–6, and from those in García 2015, due to differences in the sample sizes or to rounding.

Source: EPI analysis of ECLS-K, kindergarten class of 2010–2011 (National Center for Education Statistics)

Child and family characteristics, main developmental activities, and parental expectations for children, kindergarten classes of 1998–1999 and 2010–2011, by socioeconomic status (SES)

1998–1999		Low-SES (quintile 1)	Low-middle SES (quintile 2)	Middle SES (quintile 3)	High-middle SES (quintile 4)	High-SES (quintile 5)	All quintiles
Child and family characteristics and main developmental activities
Race/ethnicity	White	26.40%	53.70%	61.20%	68.10%	78.80%	57.70%
	Black	26.20%	17.80%	15.50%	12.00%	6.40%	15.60%
	Hispanic	39.80%	21.20%	15.80%	12.70%	6.80%	19.20%
	Hispanic English language learner (ELL)	28.40%	9.50%	4.80%	3.10%	1.40%	9.40%
	Hispanic English speaker	11.50%	11.70%	10.90%	9.60%	5.40%	9.80%
	Asian	2.30%	1.70%	2.30%	2.70%	4.70%	2.70%
	Other	5.30%	5.60%	5.30%	4.40%	3.40%	4.80%
Poverty status	Lives in poverty	71.30%	22.30%	10.60%	4.20%	1.10%	21.80%
Language	Child’s language at home is not English	31.20%	12.00%	7.00%	6.10%	5.30%	12.30%
Family composition	Not living with two parents	45.60%	30.50%	23.80%	15.80%	11.10%	25.10%
	Number of family members	4.84	4.55	4.42	4.36	4.40	4.51
	First- or second-generation immigrant	30.30%	15.10%	12.80%	13.10%	15.40%	17.30%
Pre-K care arrangements	Pre-K care	64.20%	70.90%	76.50%	81.00%	87.80%	76.20%
	Pre-K care, center-based	43.70%	45.00%	50.20%	55.40%	65.80%	52.20%
	Parental care	30.50%	22.60%	17.20%	15.40%	9.90%	18.90%
	Care by relative	15.90%	18.30%	16.20%	11.80%	6.60%	13.70%
	Care by nonrelative	5.30%	8.20%	10.90%	11.60%	13.70%	10.00%
	Care by multiple sources	4.60%	5.90%	5.50%	5.80%	3.90%	5.20%
Activities indices	Literacy/reading	-0.221	-0.059	-0.010	0.070	0.193	-0.003
	Other educational and engagement activities	-0.114	-0.011	0.014	0.042	0.071	0.002
Number of books	Average number	32.4	58.1	74.3	87.9	107.3	72.5
Number of books, grouped by least to most	0–25	61.70%	31.60%	20.20%	11.30%	5.00%	25.50%
	26–50	23.10%	34.80%	30.80%	30.60%	21.40%	28.20%
	51–100	11.30%	23.40%	32.90%	36.00%	41.00%	29.10%
	101–199	1.80%	4.00%	5.70%	6.60%	9.50%	5.60%
	More than 200	2.10%	6.20%	10.30%	15.50%	23.00%	11.50%
Parents’ expectations for their children’s educational attainment
Highest education level expected	High school or less	24.10%	15.20%	7.70%	3.70%	1.20%	10.20%
	Two or more years of college, vocational school	16.40%	21.80%	21.40%	11.60%	3.80%	14.90%
	Bachelor’s degree	33.20%	38.70%	46.70%	58.80%	57.20%	47.10%
	Master’s degree	9.20%	9.40%	10.30%	13.60%	22.80%	13.10%
	Ph.D. or M.D.	17.10%	15.00%	13.90%	12.30%	15.00%	14.60%

2010–2011		Low-SES (quintile 1)	Low-middle SES (quintile 2)	Middle SES (quintile 3)	High-middle SES (quintile 4)	High-SES (quintile 5)	All quintiles
Child and family characteristics, and main developmental activities
Race/ethnicity	White	23.10%	45.50%	56.80%	69.00%	71.30%	52.90%
	Black	19.60%	17.00%	13.40%	9.40%	5.80%	13.20%
	Hispanic	50.40%	28.30%	19.70%	12.20%	8.60%	24.10%
	Hispanic English language learner (ELL)	36.10%	11.90%	5.20%	2.10%	0.90%	11.40%
	Hispanic English speaker	14.30%	16.30%	14.40%	10.10%	7.70%	12.60%
	Asian	2.50%	2.80%	3.20%	4.40%	8.70%	4.20%
	Others	4.40%	6.40%	7.00%	4.90%	5.60%	5.70%
Poverty status	Lives in poverty	84.60%	35.70%	10.90%	3.10%	0.60%	25.50%
Language	Child’s language at home is not English	40.30%	15.60%	8.00%	5.00%	7.00%	15.30%
Family composition	Not living with two parents	54.90%	41.70%	34.10%	19.30%	9.60%	31.80%
	Number of family members	4.81	4.62	4.53	4.44	4.46	4.57
	First- or second-generation immigrant	49.80%	25.70%	18.90%	17.20%	21.60%	26.10%
Pre-K care arrangements	Pre-K care	66.60%	75.60%	81.60%	85.00%	88.30%	79.30%
	Pre-K care, center-based	44.30%	47.00%	53.10%	61.60%	69.90%	55.10%
	Parental care	34.90%	25.40%	19.10%	15.40%	12.00%	21.40%
	Care by relative	16.00%	19.70%	17.40%	12.70%	8.60%	14.90%
	Care by nonrelative	3.30%	5.50%	7.40%	7.30%	6.90%	6.10%
	Care by multiple sources	1.50%	2.40%	3.10%	2.90%	2.70%	2.50%
Activities indices	Literacy/reading	-0.231	-0.038	0.033	0.094	0.171	0.008
	Other educational and engagement activities	-0.049	0.022	0.029	0.026	0.001	0.006
Number of books	Average number	35.2	57.6	74.1	90.8	106.3	73.1
Number of books, grouped by least to most	0–25	59.30%	33.60%	19.40%	11.50%	5.00%	25.50%
	26–50	24.70%	31.70%	32.50%	26.90%	22.40%	27.70%
	51–100	11.20%	24.80%	32.30%	39.00%	41.70%	30.00%
	101–199	1.70%	3.10%	5.50%	6.50%	7.70%	4.90%
	More than 200	3.10%	6.80%	10.30%	16.20%	23.20%	12.00%
Parents’ expectations for their children’s educational attainment
Highest education level expected	High school or less	11.40%	6.20%	5.00%	2.40%	1.00%	5.20%
	Two or more years of college, vocational school	16.70%	25.00%	17.20%	9.80%	3.20%	14.40%
	Bachelor’s degree	34.80%	39.10%	47.00%	57.10%	53.10%	46.30%
	Master’s degree	10.70%	12.30%	14.60%	16.80%	26.60%	16.20%
	Ph.D. or M.D.	26.40%	17.30%	16.20%	13.90%	16.10%	17.90%

Note: SES refers to socioeconomic status.

Reading and math skills gaps between high-SES and low-SES children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010, under unadjusted to fully adjusted models

	Reading models				Mathematics models
	1 (unadjusted)	2	3	4 (fully adjusted)	1 (unadjusted)	2	3	4 (fully adjusted)
Gap in 1998	1.071***	0.846***	0.641***	0.596***	1.258***	0.932***	0.668***	0.610***
	(0.024)	(0.032)	(0.031)	(0.031)	(0.022)	(0.033)	(0.030)	(0.031)
Change in gap by 2010	0.098***	0.122***	0.096*	0.080	-0.008	0.025	0.053	0.051
	(0.033)	(0.046)	(0.051)	(0.052)	(0.032)	(0.045)	(0.047)	(0.048)
Controls
Demographics	No	No	Yes	Yes	No	No	Yes	Yes
Education and engagement	No	No	Yes	Yes	No	No	Yes	Yes
Parental expectations	No	No	No	Yes	No	No	No	Yes
School fixed effects	No	Yes	Yes	Yes	No	Yes	Yes	Yes
Observations	30,950	30,950	26,050	26,050	31,850	31,850	26,890	26,890
Adjusted R2	0.152	0.243	0.289	0.293	0.189	0.265	0.331	0.336

Notes: Models 1 and 2 use the full sample; Models 3 and 4 use the complete cases sample. Robust standard errors are in parentheses. For statistical significance, *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1. The number of observations is rounded to the nearest multiple of 10. SES refers to socioeconomic status.

Noncognitive skills gaps between high-SES and low-SES children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010, under unadjusted to fully adjusted models

	Self-control (reported by teachers) models				Approaches to learning (reported by teachers) models
	1 (unadjusted)	2	3	4 (fully adjusted)	1 (unadjusted)	2	3	4 (fully adjusted)
Gap in 1998	0.394***	0.304***	0.217***	0.182***	0.630***	0.630***	0.493***	0.435***
	(0.025)	(0.037)	(0.037)	(0.038)	(0.024)	(0.035)	(0.036)	(0.037)
Change in gap by 2010	-0.009	0.065	0.078	0.085	-0.117***	-0.066	-0.042	-0.043
	(0.037)	(0.054)	(0.060)	(0.061)	(0.035)	(0.053)	(0.057)	(0.057)
Controls
Demographics	No	No	Yes	Yes	No	No	Yes	Yes
Education and engagement	No	No	Yes	Yes	No	No	Yes	Yes
Parental expectations	No	No	No	Yes	No	No	No	Yes
School fixed effects	No	Yes	Yes	Yes	No	Yes	Yes	Yes
Observations	29,500	29,500	25,080	25,080	31,260	31,260	26,460	26,460
Adjusted R2	0.019	0.117	0.173	0.175	0.040	0.117	0.199	0.204

	Self-control (reported by parents) models				Approaches to learning (reported by parents) models
	1 (unadjusted)	2	3	4 (fully adjusted)	1 (unadjusted)	2	3	4 (fully adjusted)
Gap in 1998	0.467***	0.424***	0.357***	0.291***	0.539***	0.479***	0.215***	0.132***
	(0.025)	(0.036)	(0.039)	(0.040)	(0.025)	(0.032)	(0.033)	(0.033)
Change in gap by 2010	-0.076**	-0.084	-0.032	0.001	0.024	-0.024	0.096*	0.112**
	(0.037)	(0.054)	(0.060)	(0.061)	(0.036)	(0.053)	(0.055)	(0.056)
Controls
Demographics	No	No	Yes	Yes	No	No	Yes	Yes
Education and engagement	No	No	Yes	Yes	No	No	Yes	Yes
Parental expectations	No	No	No	Yes	No	No	No	Yes
School fixed effects	No	Yes	Yes	Yes	No	Yes	Yes	Yes
Observations	30,400	30,400	27,220	27,220	30,420	30,420	27,240	27,240
Adjusted R2	0.022	0.037	0.075	0.079	0.035	0.057	0.218	0.228

Reductions in skills gaps between high-SES and low-SES children after accounting for missingness and covariates, 1998 and 2010

	Year	Reduction	Change in reduction from 1998 to 2010 (in percentage points)
	Year	Reduction
Reading	1998	45.5%
	2010	42.9%	-2.6
Math	1998	52.6%
	2010	48.6%	-4.1
Self-control (reported by teachers)	1998	50.8%
	2010	32.6%	-18.1
Approaches to learning (reported by teachers)	1998	28.3%
	2010	20.3%	-8
Self-control (reported by parents)	1998	35.3%
	2010	34.3%	-1.1
Approaches to learning (reported by parents)	1998	73.5%
	2010	56.0%	-17.5

Note: SES refers to socioeconomic status. Declining values from 1998 to 2010 indicate that factors such as early literacy activities and other controls were not as effective at shrinking SES-based gaps in 2010 as they were in 1998.

Summary of association between cognitive and noncognitive skills at kindergarten entry and selected early educational practices, fully adjusted differences (Model 4)

	Reading	Math	Self-control (reported by teachers)	Approaches to learning (reported by teachers)	Self-control (reported by parents)	Approaches to learning (reported by parents)
Correlations between selected practices and skills measured at kindergarten entry in 1998
Center-based pre-K	0.106***	0.097***	-0.125***	-0.001	-0.006	0.018
	(0.016)	(0.015)	(0.018)	(0.018)	(0.019)	(0.016)
Number of books	0.012***	0.016***	0.004**	0.008***	0.002	0.006***
	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)	(0.002)
Reading/literacy	0.166***	0.068***	0.010	0.030*	0.143***	0.315***
	(0.016)	(0.015)	(0.018)	(0.016)	(0.018)	(0.017)
Other activities	-0.115***	-0.036***	0.047***	0.033**	0.046***	0.292***
	(0.015)	(0.014)	(0.017)	(0.016)	(0.017)	(0.016)
Correlations between parents’ expectations about their children’s highest level of educational attainment and skills measured at kindergarten entry in 1998
Two or more years of college/vocational school	0.029	0.066**	0.072*	0.115***	0.180***	0.136***
	(0.025)	(0.026)	(0.042)	(0.037)	(0.038)	(0.033)
Bachelor’s degree	0.114***	0.172***	0.141***	0.211***	0.272***	0.228***
	(0.023)	(0.023)	(0.036)	(0.032)	(0.036)	(0.030)
Master’s degree or more	0.160***	0.220***	0.120***	0.219***	0.254***	0.377***
	(0.026)	(0.025)	(0.039)	(0.034)	(0.036)	(0.033)
Changes from 1998 to 2010 in the correlations between selected practices and skills measured at kindergarten entry
Center-based pre-K	-0.005	-0.036	0.060*	-0.010	-0.020	0.010
	(0.025)	(0.025)	(0.032)	(0.031)	(0.031)	(0.026)
Number of books	0.002	-0.001	0.001	0.002	-0.002	0.004
	(0.003)	(0.002)	(0.003)	(0.003)	(0.003)	(0.002)
Reading/literacy	0.018	0.008	0.015	0.014	-0.079***	-0.173***
	(0.025)	(0.024)	(0.031)	(0.028)	(0.030)	(0.027)
Other activities	-0.008	-0.016	0.031	0.020	0.218***	0.265***
	(0.025)	(0.024)	(0.029)	(0.028)	(0.029)	(0.025)
Changes from 1998 to 2010 in the correlations between parents’ expectations about their children’s highest level of educational attainment and skills measured at kindergarten entry
Two or more years of college/vocational school	0.121**	0.106*	0.201**	0.204***	-0.030	0.151**
	(0.055)	(0.059)	(0.081)	(0.072)	(0.084)	(0.066)
Bachelor’s degree	0.139***	0.103**	0.136*	0.174***	-0.084	0.100
	(0.048)	(0.051)	(0.070)	(0.063)	(0.078)	(0.061)
Master’s degree or more	0.186***	0.117**	0.140*	0.189***	-0.041	0.076
	(0.052)	(0.054)	(0.074)	(0.066)	(0.081)	(0.063)
Observations	26,050	26,890	25,080	26,460	27,220	27,240
Adj.R2	0.293	0.336	0.175	0.204	0.079	0.228

Notes: The robust standard errors are in parentheses. For statistical significance, *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1. The number of observations is rounded to the nearest multiple of 10.

Cognitive and noncognitive skills gaps between high-SES and low-SES children using mother's educational attainment as a proxy for socioeconomic status (SES), under unadjusted and fully adjusted models

	Reading		Math		Self-control (reported by teachers)		Approaches to learning (reported by teachers)		Self-control (reported by parents)		Approaches to learning (reported by parents)
	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)
Gap in 1998	1.294***	0.696***	1.457***	0.681***	0.317***	0.076	0.638***	0.409***	0.471***	0.254***	0.655***	0.221***
	(0.038)	(0.058)	(0.036)	(0.050)	(0.039)	(0.048)	(0.038)	(0.042)	(0.039)	(0.049)	(0.039)	(0.045)
Change in gap by 2010	-0.020	-0.075	-0.154***	-0.119*	-0.099*	0.046	-0.237***	-0.141*	-0.136**	-0.093	-0.084	-0.004
	(0.051)	(0.082)	(0.049)	(0.070)	(0.055)	(0.081)	(0.053)	(0.074)	(0.053)	(0.080)	(0.053)	(0.070)
Controls
Demographics	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
Education and engagement	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
Parental expectations	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
School fixed effects	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
Observations	26,660	23,880	27,570	24,710	25,790	23,170	27,200	24,380	27,280	25,040	27,290	25,050
Adjusted R2	0.134	0.282	0.166	0.328	0.009	0.172	0.029	0.199	0.017	0.079	0.032	0.223

Notes: Model 1 uses the full sample; Model 4 uses the complete cases sample. Robust standard errors are in parentheses. For statistical significance, *** denotes p < 0.01, ** denotes p < 0.05, and * denotes p < 0.1. The number of observations is rounded to the nearest multiple of 10.

Cognitive and noncognitive skills gaps between high-SES and low-SES children using household income as a proxy for socioeconomic status (SES), under unadjusted and fully adjusted models

	Reading		Math		Self-control (reported by teachers)		Approaches to learning (reported by teachers)		Self-control (reported by parents)		Approaches to learning (reported by parents)
	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)
Gap in 1998	1.090***	0.384***	1.308***	0.443***	0.419***	0.119**	0.603***	0.325***	0.443***	0.272***	0.436***	0.073
	(0.042)	(0.058)	(0.041)	(0.060)	(0.045)	(0.050)	(0.044)	(0.049)	(0.045)	(0.051)	(0.044)	(0.052)
Change in gap by 2010	-0.127**	-0.006	-0.230***	-0.060	0.049	0.228***	-0.128**	0.008	0.044	0.106	0.032	0.051
	(0.060)	(0.084)	(0.059)	(0.082)	(0.066)	(0.081)	(0.064)	(0.079)	(0.065)	(0.084)	(0.064)	(0.080)
Controls
Demographics	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
Education and engagement	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
Parental expectations	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
School fixed effects	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
Observations	28,650	26,050	29,560	26,890	27,550	25,080	29,110	26,460	28,170	27,220	28,190	27,240
Adjusted R2	0.103	0.276	0.143	0.321	0.023	0.174	0.036	0.199	0.019	0.079	0.019	0.226

Cognitive and noncognitive skills gaps between high-SES and low-SES children using number of books child has in the home as a proxy for socioeconomic status, under unadjusted and fully adjusted models

	Reading		Math		Self-control (reported by teachers)		Approaches to learning (reported by teachers)		Self-control (reported by parents)		Approaches to learning (reported by parents)
	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)	1 (unadjusted)	4 (fully adjusted)
Gap in 1998	0.736***	0.347***	0.966***	0.424***	0.324***	0.105***	0.455***	0.241***	0.283***	0.117***	0.583***	0.136***
	(0.028)	(0.034)	(0.027)	(0.031)	(0.029)	(0.035)	(0.028)	(0.033)	(0.029)	(0.037)	(0.028)	(0.033)
Change in gap by 2010	0.083**	-0.540***	-0.019	-0.818***	-0.068	-0.126	-0.058	-0.244	-0.044	-0.248	0.085**	-0.026
	(0.039)	(0.184)	(0.038)	(0.188)	(0.042)	(0.225)	(0.041)	(0.184)	(0.041)	(0.216)	(0.039)	(0.178)
Controls
Demographics	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
Education and engagement	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
Parental expectations	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
School fixed effects	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
Observations	29,060	26,050	29,920	26,890	27,730	25,080	29,350	26,460	30,200	27,220	30,220	27,240
Adjusted R2	0.080	0.270	0.120	0.314	0.012	0.172	0.024	0.194	0.009	0.075	0.047	0.226

'Whole-child' case study initiatives, by service area

Part of school district	Entire school district	Across multiple school districts
Austin, Texas	Joplin, Missouri	Eastern Kentucky*
Boston, Massachusetts	Kalamazoo, Michigan
Durham, North Carolina (East Durham)	Montgomery County, Maryland*
Minneapolis, Minnesota (North Minneapolis)	Pea Ridge, Arkansas
New York, New York	Vancouver, Washington**
Orange County, Florida (Tangelo Park)

*Indicates that while the initiative covers the entire county or region, a portion of the county or region receives more intensive services. **Indicates that the initiative will cover the entire school district under plans to expand.

Source: Case studies published on the Broader, Bolder Approach to Education website (www.boldapproach.org/case-studies)

1. Values are in 2008 dollars.

2. Early investments in education strongly predict adolescent and adult development (Cunha and Heckman 2007; Heckman 2008; Heckman and Kautz 2012). For instance, students with higher levels of behavioral skills learn more in school than peers whose attitudinal skills are less developed (Jennings and DiPrete 2010). In general, as Heckman asserted, “skills beget skills,” meaning that creating basic, foundational knowledge makes it easier to acquire skills in the future (Heckman 2008). Conversely, children who fail to acquire this early foundational knowledge may experience some permanent loss of opportunities to achieve to their full potential. Indeed, scholars have documented a correlation between lack of kindergarten readiness and not reading well at third grade, which is a key point at which failing to read well greatly reduces a child’s odds of completing high school (Fiester 2010; Hernandez 2011).

3. Research by Reardon (2011) had found systematic increases in income gaps among generations. Recent studies by Bassok and Latham (2016) and Reardon and Portilla (2016), however, show narrower achievement gaps at kindergarten entry between a recent cohort and the previous one, and thus a possible discontinuation or interruption of that trend. (Bassok et al. [2016] use an SES construct to compare relative teacher assessments of cognitive and behavioral skills among low-SES children versus all children, adjusted by various other characteristics; Reardon and Portilla [2016] look at relative performance of children in the 90th and 10th income percentiles, and use age-adjusted, standardized, outcome scores.) Research by Carnoy and García (2017) shows persistent social-class gaps, but no solid evidence regarding trends: their findings for students in the fourth and eighth grades, in math and reading, show that achievement gaps neither shrink nor grow consistently (they are a function of the social-class indicator, the grade level, or the subject).

4. Clustering takes into account the fact that children are not randomly distributed, but tend to be concentrated in schools or classrooms with children of the same race, social class, etc. These estimates offer an estimate of gaps within schools. See Appendix B for more details.

5. Results available upon request. See García 2015 for results for all SES-quintiles (the baseline or unadjusted gaps in that report correspond with Model 2 in this paper).

6. The Early Childhood Longitudinal Study asks both parents and teachers to rate children’s abilities across a range of these skills. The specific skills measured may vary between the home and classroom setting. Teachers likely evaluate their students’ skills levels relative to those of other children they teach. Parents, on the other hand, may be basing their expectations on family, community, culture, or other factors.

7. See García 2015 for a discussion of which factors in children’s early lives and their individual and family characteristics (in addition to social class) drive the gaps among children of the 2010 kindergarten class.

8. Note that the SES quintiles are constructed using each year’s distribution, and that changes in the overall and relative distribution may affect the characteristics of children in the different quintiles each year (i.e., there may be some groups who are relatively overrepresented in one or another quintile if changes in the SES components changed over time).

9. The detailed frequency with which parents develop or practice some activities with their children at home and others is available upon request.

10. Literature on expectations and on parental behaviors in the home find that they positively correlate with children’s cognitive development and outcomes (Simpkins, Davis-Kean, and Eccles 2005; Wentzel, Russell, and Baker 2016). This literature acknowledges the multiple pathways through which expectations and behaviors influence educational outcomes, as well as the importance of race, social class, and other factors as moderators of such associations (Davis-Kean 2005; Redd et al. 2004; Wentzel, Russell, and Baker 2016; Yamamoto and Holloway 2010).

11. This may be affected by the fact that the highest number of reported books in 1998 was “more than 200,” while in 2010 parents could choose from more categories, up to “more than 1,000.” We had to use 200 as our cap in order to compare data for the two kindergarten classes.

12. Evidence also points to many other factors that affect children’s school readiness, and these, too, likely changed over this time period. For example, access to prenatal care, health screenings, and nutritional programs could all have affected children’s development differently across these two cohorts, but we do not have access to these data and thus cannot control for them in our study. For links between school readiness, children’s health, and poverty, see AAP COCP 2016; Currie 2009; U.S. HHS and U.S. ED 2016.

13. Models include all quintiles in their specification. Tables that offer a comparison for all quintiles relative to the first quintile are available upon request. We focus the discussion on the gap between the top and bottom.

14. As a result, sample sizes become smaller (see Appendix Table C1). Assuming “missingness” (observations without full information) is completely at random, the findings are representative of the original sample and of the populations they represent. Analytic samples once missingness is accounted for are called the complete case samples. We tested to see whether the unadjusted gaps estimated above with the full sample remained the same when using the complete case samples. For Model 1, we found an average difference of 0.01 sd in the estimates of 1998 SES gaps, and an average difference of 0.02 sd in the estimates of the change in the gaps. For Model 2, the differences were 0.01 sd for the gaps’ estimates and 0.04 for changes in the gaps’ estimates. In terms of statistical significance, there are no significant changes in the estimates associated with the 1998 gaps, but there are two changes in the statistical significance of the estimates associated with the changes in the gaps by 2010 – 2011, and one change in the magnitude of the coefficient. The first change in the statistical significance of the estimates associated with the changes in the gaps by 2010 – 2011 is the change in the gap in approaches to learning as reported by parents, which is statistically significant when using the restricted sample (0.07 sd, at the 10 percent significance level, Model 1); and the second is the change in the gap in math which also becomes statistically significant when using the restricted sample (0.09, at the 10 percent significance level, Model 2). Finally, the one change in the magnitude of the coefficient, in this model, is the estimate of the change in the gap in reading, which increases when using the restricted sample (from 0.12 sd to 0.18 sd). Results are available upon request.

15. These interactions between inputs and time test for whether the influence of inputs in 2010 is smaller than, the same as, or larger than the influence of inputs in 1998. Also, although only the fully specified results are shown, as noted in Appendix B, these sets of controls are entered parsimoniously in order to determine how sensitive gaps and changes in gaps over time are to the inclusion of family characteristics only, to the added inclusion of family investments, and, finally, to the inclusion of parental expectations (for the inclusion of parental expectations, we incorporated interactions of the covariates with time parsimoniously as well). For all outcomes, and focusing on the models without interactions between covariates and time, we find that all gaps in 1998 continuously shrink as we add more controls. For example, in reading, adding family characteristics reduces the gap in 1998 by 11 percent, adding investments further reduces it by 15 percent, and adding expectations further reduces it by 9 percent. In math, these changes equal to 16 percent, 13 percent, and 10 percent. For changes in the gap by 2010–2011, for both reading and math, adding family characteristics and investments shrink the changes in the gaps, but adding expectations slightly increases the estimated coefficients (which are statistically significant for reading, but not for math in these models. For self-control (as reported by teachers) and approaches to learning (by parents), which are the only two noncognitive skills for which the change in the gap is statistically significant, adding family characteristics reduces the change in the “gap [by 2010–2011” coefficient], but adding investments increases it, and adding expectations further increases the changes in the gaps by 2010–2011. These results are not shown in the appendices, but are available upon request.

16. The interactions between parental expectations of children’s educational attainment and the time variable test for whether the influence of expectations in 2010 is smaller, the same, or larger, than the influence of expectations in 1998.

17. The change in the skills gaps by SES in 2010 due to the inclusion of the controls is not directly visible in the tables in this report. To see this, see the comparison of estimates of models MS1–MS3 in García 2015. The change in the skills gaps by SES in 1998 is directly observable in Tables 3 and 4 and is discussed below.

18. The numbers in the “Reduction” column in Table 5 (showing the shares of the SES-based skills gaps that are accounted for by controls) are always higher for 1998 than for 2010.

19. Please note that until this point in the report we have been concerned with SES gaps and not with performance directly (though SES gaps are the result of the influence of SES on performance, which leads to differential performance of children by SES and hence to a performance gap). The paragraphs above emphasize how controls mediate or explain some of the skills gaps by SES, so, in a way, controls inform our analysis of gaps because they reveal how changes in gaps may have been affected by changes in various factors’ capacity to influence performance. Now the focus is on exploring the independent effect of the covariates of interest on performance. In this report, because we address whether the education and selected practices affect outcomes, the main effect is measured for the 1998 cohort, and we measure how it changed between 1998 and 2010. The detailed discussion for the correlation between covariates and outcomes in 2010 is provided in Table 3 in García 2015.

20. This variable indicates whether the child was cared for in a center-based setting during the year prior to the kindergarten year, compared with other options (as explained in García 2015, these alternatives include no nonparental care arrangements; being looked after by a relative, a nonrelative, at home or outside; or a combination of options. Any finding associated with this variable may be interpreted as the association between attending prekindergarten programs, compared with other options, but must be interpreted with caution. In other words, the child may have attended a high-quality prekindergarten program, which could have been either private or public, or a low-quality one, which would have different impacts. He or she might have been placed in (noneducational) child care, either private or public, of high or low quality, for few or many hours per day, with very different implications for his or her development (Barnett 2008; Barnett 2011; Magnuson et al. 2004; Magnuson, Ruhm, and Waldfogel 2007; Nores and Barnett 2010). For the extensive literature explaining the benefits of pre-K schooling, see Camilli et al. 2010, and for a meta-analysis of results, see Duncan and Magnuson 2013. Thus, more detailed information on the characteristics of the nonparental care arrangements (type, quality, and quantity) would help researchers further disentangle the importance of this variable. This additional information would provide a much clearer picture of the effects of early childhood education on the different educational outcomes.

21. Because these associations seemed counterintuitive, we tested whether they were sensitive to the composition of the index. We removed one component of the index at a time and created five alternative measures of other enrichment activities that parents do with their children. The results indicate that the negative association between the index and reading is not sensitive to the components of the index (the coefficients for the main effect, i.e., for the effect in 1998 range between -0.14 and -0.09, are all statistically significant). For math, the associations lose some precision, but retain the negative sign (negative association) in four out of the five cases (minimum coefficient is -0.06). As a caveat, these components do not reflect whether the activities are undertaken by the child or guided by the adult, the time devoted to them, or how much they involve the use of vocabulary or math concepts. The associations could indicate that time spent on nonacademic activities detracts from parents’ time to spend on activities that are intended to boost their reading and math skills, among other possible explanations. These results are available upon request.

22. Note that in this section, “social class” and “socioeconomic status” (SES) are treated as equivalent terms; in the rest of the report, we refer to SES as a construct that is one measure of social class. See Appendices C and D for discussions of two other sensitivity analyses, one based on imputation of missing values for the main analysis in this paper, and the other on the utilization of various metrics of the cognitive variables. Overall, our findings were not sensitive to various multiple imputation tests. In terms of the utilization of different metrics for the cognitive variables, some sensitivity of the point estimates was detected.

23. With certain activities that are already so provided to high-SES children, there may be little room for doing more for them. For example, there are only 24 hours per day to read to your child, so there is a cap on reading from a cap on time. But perhaps there is still room to improve the influence of reading, if, for example, the way reading is done changes.

24. Eight of the 12 districts explored in this paper are the subjects of published case studies. Case studies for the other four are in progress and will be published later this year. When citing information from the published case studies, we cite the specific published study. For the four that are not yet published, we refer to the original sources being used to develop the case studies.

25. Missing or incomplete cells in the table indicate that data were not available on that aspect of student demographics or other characteristics. As per the source note, most data came either from the districts’ websites or from NCES.

26. In the country as a whole, poverty rates, which had been rising prior to 2007, sped up rapidly during the recession and in its aftermath (through 2011–2012), and minority students (mainly Hispanic and Asian) grew as a share of the U.S. public school student body. Between 2000 and 2013, even with a decline in the proportion of black students, the share of the student body that is minority (of black or Hispanic origin) increased from 30.0 percent to 40.5 percent, and the proportion of low-income students (those eligible for free or reduced-price lunch) also increased, up from 38.3 percent of all public school students in 2000 to 52.0 percent in 2013 (Carnoy and García 2017). The Southern Education Foundation revealed a troubling tipping point in 2013: for the first time since such data have been collected, over half of all public school students (51 percent) qualified for free or reduced-priced meals (i.e., over half of students were living in households at or below 185 percent of the federal poverty line). Across the South, shares were much higher, with the highest percentage, 71 percent—or nearly three in four students—in Mississippi (Southern Education Foundation 2015).

27. A full cross-cutting analysis of why and how these districts have employed whole-child/comprehensive educational approaches will be published as part of a book that draws on these case studies.

28. The federal Early Head Start (EHS) program includes both a home visiting and a center-based component, with many of the low-income infants and toddlers served benefiting from a combination of the two. Studies of EHS find improved cognitive, behavioral, and emotional skills for children as well as enhanced parenting behaviors.

29. According to one important source for data on access to and quality of state pre-K programs, the State of Preschool yearbook produced annually by the National Institute for Early Education Research (NIEER) at Rutgers University, as of 2015, 42 states and the District of Columbia were funding 57 programs. Moreover, programs continued to recover from cuts made during the Great Recession; enrollment, quality, and per-pupil spending were all up, on average, compared with the year before, albeit with the important caveat that two major states—Texas and Florida—lost ground, and that “[f]or the nation as a whole,…access to a high-quality preschool program remained highly unequal, and this situation is unlikely to change in the foreseeable future unless many more states follow the leaders” (NIEER 2016).

30. Elaine Weiss interview with Joshua Starr, June 2017.

31. Murnane and Levy 1996; Elaine Weiss interview with Joshua Starr, June 2017.

32. In recent years, a growing number of reports have emerged that some charter schools—which are technically public schools and often tout their successes in serving disadvantaged students—keep out students unlikely to succeed through complex application processes, fees, parent participation contracts, and other mechanisms, and then further winnow the student body of such students by pushing them out when they struggle academically or behaviorally. For more on this topic, see Burris 2017, PBS NewsHour 2015, and Simon 2013.

33. See AIR 2011 and Sparks 2017. The federal school improvement models, in order of severity (from lightest to most stringent) are termed “transformation,” “turnaround,” “restart,” and “closure” (AIR 2011, 3).

34. While the cut score on any given assessment/test needed for a student to be considered “proficient” is an arbitrary one, and, in Minnesota and many other states, changes from year to year and from one assessment to another, these gains are a helpful indicator of program effectiveness, as they are comparable over the time period described.

35. Joplin statistics are from internal data produced for the superintendent at that time that are no longer available.

36. Attendance Works , a national campaign to reduce chronic absence, points to a range of studies that document and explain the connections between chronic absenteeism, student physical and mental health, and student achievement. Areas of research include elementary school absenteeism, middle and high school absenteeism, health issues, and state and local data on how these problems play out, among others.

37. Elaine Weiss interview with C.J. Huff, June 2016.

38. See Appendix D for a discussion of results using other metrics for reading and math achievement. Results are not meaningfully different across metrics, though the point estimates differ slightly.

39. This last feature will be explored in a companion paper to this one, as soon as the necessary information is released by NCES. (As Tourangeau et al. [2013] note, the assessment scores for the 2010–2011 cohort are not directly comparable with those for the 1998–1999 cohort. We are waiting on the availability of this data to conduct a companion study that allows us to learn whether starting levels of knowledge rose over these years, and what the relative gains were for different demographic groups.)

40. We acknowledge that there are multiple noneducation public policy and economic policy areas to be called upon to address the problems studied in this report, namely, all the ones that ensure other factors that correlate with low-SES are attended, and, obviously, the ones that lead to fewer low-SES children. These other policies could help ensure that more children grow up in contexts with sufficient resources and healthy surroundings, or would leave fewer children without built-in supports at home that need to be compensated for afterwards. We made these points in two early studies, and in the policy brief companion to this study (García 2015; García and Weiss 2015; García and Weiss 2017). A similar comprehensive approach in terms of policy recommendations was used by Putnam (2015).

AAP Council on Community Pediatrics (AAP COCP). 2016. “Poverty and Child Health in the United States.” Pediatrics vol. 137, no. 4. pii:e20160339.

Adamson, Frank, and Linda Darling-Hammond. 2012. “Funding Disparities and the Inequitable Distribution of Teachers: Evaluating Sources and Solutions.” Education Policy Analysis Archives vol. 20 (November), 37.

Alvarez, Lizette. 2015. “ One Man’s Millions Turn a Community in Florida Around .” New York Times , May 25.

American Institutes for Research (AIR). 2011. School Turnaround: A Pocket Guide .

Austin Independent School District (AISD). 2017. “ Pre-K 4 ” (section on the AISD website).

Baker, Bruce D., and Sean P. Corcoran. 2012. The Stealth Inequities of School Funding . The Center for American Progress.

Barbarin, O.A., J. Downer, E. Odom, and D. Head. 2010. “Home–School Differences in Beliefs, Support, and Control during Public Pre-Kindergarten and Their Link to Children’s Kindergarten Readiness.” Early Childhood Research Quarterly vol. 25, no. 3, 358–72.

Barnett, W. Steven. 2008. Preschool Education and Its Lasting Effects: Research and Policy Implications . Great Lakes Center for Education Research and Practice.

Barnett, W. Steven. 2011. “Effectiveness of Early Educational Intervention.” Science vol. 333, no. 6045, 975–78. doi:10.1126/science.1204534.

Barnett, W. Steven, Elizabeth Votruba-Drzal, Eric Dearing, and Megan E. Carolan. 2017. “Publicly Supported Early Care and Education Programs.” In The Wiley Handbook of Early Childhood Development Programs, Practices, and Policies , Elizabeth Votruba-Drzal and Eric Dearing, eds. Malden, Mass., and Oxford: John Wiley.

Bassok, Daphna, Jenna E. Finch, RaeHyuck Lee, Sean F. Reardon, and Jane Waldfogel. 2016. “Socioeconomic Gaps in Early Childhood Experiences: 1998 to 2010.” AERA Open vol. 2, no. 3.

Bassok, Daphna, and Scott Latham. 2016. “ Kids Today: Changes in School-Readiness in an Early Childhood Era .” EdPolicyWorks Working Paper Series no. 35.

Berea College. 2013. “ U.S. Secretary of Education Visits First Rural Promise Neighborhood ” (news release). November 12.

Bradbury, Bruce, Miles Corak, Jane Waldfogel, and Elizabeth Washbrook. 2015. Too Many Children Left Behind: The U.S. Achievement Gap in Comparative Perspective. New York: Russell Sage Foundation.

Brooks-Gunn, Jeanne, and Lisa Markman. 2005. “The Contribution of Parenting to Ethnic and Racial Gaps in School Readiness.” Future of Children vol. 15, no. 1, 139–68.

Bivens, Josh. 2016. Progressive Redistribution without Guilt. Using Policy to Shift Economic Power and Make U.S. Incomes Grow Fairer and Faster . Economic Policy Institute.

Bivens, Josh, Emma García, Elise Gould, Elaine Weiss, and Valerie Wilson. 2016. It’s Time for an Ambitious National Investment in America’s Children: Investments in Early Childhood Care and Education Would Have Enormous Benefits for Children, Families, Society, and the Economy . Economic Policy Institute.

Boston College Center for Optimized Student Support. 2012. The Impact of City Connects: Progress Report 2012 .

Boston College Center for Optimized Student Support. 2014. The Impact of City Connects: Progress Report 2014 .

Burris, Carol. 2017. “ What the Public Isn’t Told about High Performing Charter Schools in Arizona .” Washington Post Answer Sheet blog, March 30.

Camilli, Gregory, Sadako Vargas, Sharon Ryan, and W. Steven Barnett. 2010. “Meta-Analysis of the Effects of Early Education Interventions on Cognitive and Social Development.” Teachers College Record vol. 112, no. 3, 579–620.

Carnoy, Martin, and Emma García. 2017. Five Key Trends in U.S. Student Performance. Progress by Blacks and Hispanics, the Takeoff of Asians, the Stall of Non-English Speakers, the Persistence of Socioeconomic Gaps, and the Damaging Effect of Highly Segregated Schools . Economic Policy Institute.

Carter, Prudence L., and Kevin G. Welner, eds. 2013. Closing the Opportunity Gap: What America Must Do to Give Every Child an Even Chance . New York: Oxford Univ. Press.

Caspe, Margaret, and Joy Lorenzo Kennedy. 2014. Sustained Success: The Long-Term Benefits of High Quality Early Childhood Education. New York: Children’s Aid Society.

Chaudry, Ajay, Taryn Morrissey, Christina Weiland, and Hirokazu Yoshikawa. 2017. Cradle to Kindergarten: A New Plan to Combat Inequality . New York: Russell Sage Foundation.

Chetty, Raj, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. 2016. “ The Fading American Dream: Trends in Absolute Income Mobility since 1940 .” NBER Working Paper no. 22910.

Child Trends. 2014. Making the Grade: Assessing the Evidence for Integrated Student Supports .

Clark, H., et al. 2009. Study Comparing Children’s Aid Society Community Schools to Other New York City Public Schools (All Schools and Peer Schools ). ActKnowledge.

Coleman, J.S., E. Campbell, C. Hobson, J. McPartland, A. Mood, F. Weinfeld, and R. York. 1966. Equality of Educational Opportunity . Washington, D.C.: U.S. Office of Education.

Collaborative for Academic, Social, and Emotional Learning (CASEL). 2017. “ Partner Districts: Austin ” (webpage). Accessed August 31, 2017.

Cook-Harvey, C.M., L. Darling-Hammond, L. Lam, C. Mercer, and M. Roc. 2016. Equity and ESSA: Leveraging Educational Opportunity Through the Every Student Succeeds Act . Palo Alto, Calif.: Learning Policy Institute.

Cunha, Flavio, and James J. Heckman. 2007. “The Technology of Skill Formation.” American Economic Review vol. 97, no. 2, 31–47.

Currie, Janet. 2009. “Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development.” Journal of Economic Literature vol. 47, no. 1, 87–122.

Davis-Kean, Pamela E. 2005. “The Influence of Parent Education and Family Income on Child Achievement: The Indirect Role of Parental Expectations and the Home Environment.” Journal of Family Psychology vol. 19, no. 2 (June 2005), 294–304. doi:10.1037/0893-3200.19.2.294.

Duncan, Greg J., Chantelle J. Dowsett, Amy Claessens, Katherine A. Magnuson, Aletha C. Huston, Pamela Klebanov, Linda S. Pagani, Leon Feinstein, Mimi Engel, and Jeanne Brooks-Gunn. 2007. “School Readiness and Later Achievement.” Developmental Psychology vol. 43, no. 6, 1428–46.

Duncan, Greg J., and Katherine A. Magnuson. 2011. “The Nature and Impact of Early Achievement Skills, Attention Skills, and Behavior Problems.” In Whither Opportunity?: Rising Inequality, Schools, and Children’s Life Chances , Greg J. Duncan and Richard Murnane, eds., 47–69. New York: Russell Sage Foundation.

Duncan, Greg J., and Katherine Magnuson. 2013. “Investing in Preschool Programs.” Journal of Economic Perspectives vol. 27, no. 2, 109–32.

Duncan, Greg J., Pamela A. Morris, and Chris Rodrigues. 2011. “Does Money Really Matter? Estimating Impacts of Family Income on Young Children’s Achievement with Data from Random-Assignment Experiments.” Developmental Psychology vol. 47, no. 5, 1263–79. doi:10.1037/a0023875.

Duncan, Greg J., and Richard Murnane. 2011. “Introduction: The American Dream, Then and Now.” In Whither Opportunity?: Rising Inequality, Schools, and Children’s Life Chances , Greg J. Duncan and Richard Murnane, eds. New York: Russell Sage Foundation.

Economic Policy Institute (EPI). 2012. “ The Great Recession .” State of Working America feature.

Economic Policy Institute (EPI). 2013. “ Inequality.is ” (interactive website).

Elmore, Richard, David Thomas, and Tonika Cheek Clayton. 2006. Differentiated Treatment in Montgomery County Public Schools . Public Education Leadership Project at Harvard University.

Fiester, Leila. 2010. Early Warning! Why Reading by the End of Third Grade Matters. KIDS COUNT Special Report . Annie E. Casey Foundation.

García, Emma. 2015. Inequalities at the Starting Gate: Cognitive and Noncognitive Skills Gaps between 2010–2011 Kindergarten Classmates . Economic Policy Institute.

García, Emma, and Elaine Weiss. 2015. Early Education Gaps by Social Class and Race Start U.S. Children Out on Unequal Footing. A Summary of the Major Findings in Inequalities at the Starting Gate . Economic Policy Institute.

García, Emma, and Elaine Weiss. 2016. Making Whole-Child Education the Norm: How Research and Policy Initiatives Can Make Social and Emotional Skills a Focal Point of Children’s Education . Economic Policy Institute.

García, Emma, and Elaine Weiss. 2017. Key Findings from the Report “Education Inequalities at the School Starting Gate” . Economic Policy Institute.

Hart, Betty, and Todd R. Risley. 1995. Meaningful Differences in the Everyday Experience of Young American Children . Baltimore, Md.: Brookes.

Heckman, James J. 2008. “Schools, Skills, and Synapses.” Economic Inquiry vol. 46, no. 3, 289–324.

Heckman, James J., and Tim Kautz. 2012. “Hard Evidence on Soft Skills.” Labour Economics vol. 19, no. 4, 451–64.

Henderson, Anne T. 2010. Community Organizing to Build Partnerships in Schools: The Alliance Schools Movement in Austin . Annenberg Institute for School Reform.

Hernandez, Donald J. 2011. Double Jeopardy: How Third-Grade Reading Skills and Poverty Influence High School Graduation . Annie E. Casey Foundation.

Gizriel, Sarah. 2016. “ Bright Futures Looking to Expand to Schools across Shenandoah Valley .” localDVM.com , December 9.

Jennings, J.L., and T.A. DiPrete. 2010. “Teacher Effects on Social and Behavioral Skills in Early Elementary School.” Sociology of Education vol. 83, no. 2, 135.

Kalamazoo Public Schools (KPS). 2017. “ PEEP Information and Applications ” (webpage). Accessed August 31, 2017.

Lee, Valerie E., and David T. Burkam. 2002. Inequality at the Starting Gate . Washington, D.C.: Economic Policy Institute.

Levin, Henry M. 2012a. “More Than Just Test Scores.” Prospects vol. 42, no. 3, 269–84.

Levin, Henry M. 2012b. “The Utility and Need for Incorporating Noncognitive Skills into Large-scale Educational Assessments.” In The Role of International Large-Scale Assessments: Perspectives from Technology, Economy, and Educational Research , Matthias von Davier et al., eds. Springer.

Magnuson, Katherine, and Greg J. Duncan. 2016. “Can Early Childhood Interventions Decrease Inequality of Economic Opportunity?” RSF: The Russell Sage Foundation Journal of the Social Sciences vol. 2, no. 2, 123–41.

Magnuson, Katherine A., M.K. Meyers, C.J. Ruhm, and Jane Waldfogel. 2004. “Inequality in Preschool Education and School Readiness.” American Educational Research Journal vol. 41, no. 1, 115–57.

Magnuson, Katherine A., Christopher Ruhm, and Jane Waldfogel. 2007. “Does Prekindergarten Improve School Preparation and Performance?” Economics of Education Review vol. 26, no. 1, 33–51.

Marietta, Geoff. 2010. Lessons for PreK-3rd from Montgomery County Public Schools: An FCD Case Study . Foundation for Child Development.

Maryland State Department of Education. 2017. “ Judy Centers ” (webpage). Accessed August 31, 2017.

Miller-Adams, Michelle. 2015. Promise Nation: Transforming Communities through Place-Based Scholarships . Kalamazoo, Mich.: W.E. Upjohn Institute for Employment Research.

Mishel, Lawrence. 2015. “ The Opportunity Dodge .” American Prospect , April 9.

Mishel, Lawrence, Josh Bivens, Elise Gould, and Heidi Shierholz. 2012. The State of Working America, 12th Edition , An Economic Policy Institute Book. Ithaca, N.Y.: Cornell Univ. Press.

Mishel, Lawrence, and Jessica Schieder. 2016. Stock Market Headwinds Meant Less Generous Year for Some CEOs . Economic Policy Institute.

Montgomery County Public Schools (MCPS). 2015. Graduation Rate Rises, Gap Narrows for MCPS Class of 2014 (public announcement). January 27.

Montgomery County Public Schools (MCPS). 2016. Linkages to Learning (brochure).

Montgomery County Public Schools (MCPS). 2017. “ Maryland Meals for Achievement ” (webpage). Accessed August 31, 2017.

Morsy, Leila, and Richard Rothstein. 2015. Five Social Disadvantages That Depress Student Performance: Why Schools Alone Can’t Close Achievement Gaps . Economic Policy Institute.

Murnane, Richard J., and Frank Levy. 1996. Teaching the New Basic Skills: Principles for Educating Children to Thrive in a Changing Economy . New York: The Free Press.

Murnane, Richard J., John B. Willett, Kristen L. Bub, and Kathleen McCartney. 2006. “Understanding Trends in the Black-White Achievement Gaps during the First Years of School.” Brookings-Wharton Papers on Urban Affairs.

Najarian, M., K. Tourangeau, C. Nord, K. Wallner-Allen, and J. Leggitt. Forthcoming. Early Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K:2011), First-Grade and Second-Grade Psychometric Report . Washington, D.C.: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.

National Center for Education Statistics (NCES) (U.S. Department of Education). Early Childhood Longitudinal Study, Kindergarten Class of 1998–99 (ECLS-K 1998–1999) .

National Center for Education Statistics (NCES) (U.S. Department of Education). Early Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K 2010–2011) .

National Institute for Early Education Research (NIEER). 2016. The State of Preschool 2015: State Preschool Yearbook .

New York Times /CBS News. 2015. “ Americans’ Views on Income Inequality and Workers’ Rights ” (poll results). June 3.

Nores, Milagros, and W. Steven Barnett. 2010. “Benefits of Early Childhood Interventions across the World: (Under) Investing in the Very Young.” Economics of Education Review vol. 29, no. 2, 271–82.

Nores, Milagros, and W. Steven Barnett. 2014. Access to High Quality Early Care and Education: Readiness and Opportunity Gaps in America . New Brunswick, N.J.: Center on Enhancing Early Learning Outcomes.

Nores, Milagros, and Emma García. 2014. “Language, Immigration and Hispanics. Understanding Achievement Gaps in the Early Years.” Paper presented at the Association for Public Policy Analysis and Management Fall Research Conference, November 6–8, Albuquerque, N.M.

Oakes, Jeannie, Anna Maier, and Julia Daniel. 2017. Community Schools: An Evidence-Based Strategy for Equitable School Improvement , Learning Policy Institute, June 5.

PBS NewsHour . 2015. “ In Reforming New Orleans, Have Charter Schools Left Some Students Out? ” (news segment).

Peterson, T.K., ed. 2013. Expanding Minds and Opportunities: Leveraging the Power of Afterschool and Summer Learning for Student Success . Washington, D.C.: Collaborative Communications Group.

Phillips, Meredith. 2011. “Parenting, Time Use, and Disparities in Academic Outcomes.” In Whither Opportunity?: Rising Inequality, Schools, and Children’s Life Chances , Greg J. Duncan and Richard Murnane, eds. New York: Russell Sage Foundation.

Proctor, Bernadette D., Jessica L. Semega, and Melissa A. Kollar. 2016. Income and Poverty in the United States: 2015 . U.S. Census Bureau, Current Population Reports, P60-256(RV).

Putnam, Robert. 2015. Our Kids: The American Dream in Crisis . New York: Simon and Schuster.

Ready, Douglas D. 2010. “Socioeconomic Disadvantage, School Attendance, and Early Cognitive Development.” Sociology of Education vol. 83, no. 4, 271–86.

Reardon, Sean F. 2007. “ Thirteen Ways of Looking at the Black-White Test Score Gap .” Working paper.

Reardon, Sean F. 2011. “The Widening Academic Achievement Gap between the Rich and the Poor: New Evidence and Possible Explanations.” In Whither Opportunity?: Rising Inequality, Schools, and Children’s Life Chances , Greg J. Duncan and Richard Murnane, eds. New York: Russell Sage Foundation.

Reardon, Sean F., and Ximena A. Portilla. 2016. “Recent Trends in Income, Racial, and Ethnic School Readiness Gaps at Kindergarten Entry.” AERA Open vol. 2, no. 3, 1–18. doi: 10.1177/2332858416657343.

Redd, Z., L. Guzman, L. Lippman, L. Scott, and G. Matthews. 2004. Parental Expectations for Children’s Educational Attainment: A Review of the Literature . Prepared by Child Trends for the National Center for Education Statistics.

Rolnick, Art, and Rob Grunewald. 2003. “Early Childhood Development: Economic Development with a High Public Return.” The Region vol. 17, no. 4, 6–12.

Rothstein, Richard. 2004. Class and Schools: Using Social, Economic, and Educational Reform to Close the Achievement Gap . Washington, D.C.: Economic Policy Institute; New York: Columbia University Teachers College.

Rothstein, Richard. 2010. “Family Environment in the Production of Schooling.” In International Encyclopedia of Education , Dominic J. Brewer, Patrick J. McEwan, eds. Oxford: Elsevier. doi: 10.1016/B978-0-08-044894-7.01233-1.

Saez, Emmanuel. 2016. Striking It Richer: The Evolution of Top Incomes in the United States (Updated with 2015 Preliminary Estimates) .

Schanzenbach, Diane, Megan Mumford, Ryan Nunn, and Lauren Bauer. 2016. Money Lightens the Load . The Hamilton Project, Brookings Institute.

Selzer, Michael H., Ken A. Frank, and Anthony S. Bryk. 1994. “The Metric Matters: The Sensitivity of Conclusions about Growth in Student Achievement to Choice of Metric.” Educational Evaluation and Policy Analysis vol. 16, 41–49.

Sharkey, Patrick. 2013. Stuck in Place: Urban Neighborhoods and the End of Progress toward Racial Equality . Chicago, Ill.: Univ. of Chicago Press.

Simon, Stephanie. 2013. “ Class Struggle: How Charter Schools Get Students They Want .” Reuters , February 15.

Simpkins, Sandra D., Pamela E. Davis-Kean, and Jacquelynne S. Eccles. 2005. “Parents’ Socializing Behavior and Children’s Participation in Math, Science, and Computer Out-of-School Activities.” Applied Developmental Science vol. 9, no. 1, 14–30. doi:10.1207/s1532480xads0901_3.

Southern Education Foundation. 2015. A New Majority: Low-Income Students Now a Majority in the Nation’s Public Schools . January.

Sparks, Sarah D. 2017. “Billions in School Improvement Spending but Not Much Student Improvement.” EdWeek , January 19.

StataCorp. 2015. Stata: Release 14 [statistical software]. College Station, Texas: StataCorp LP.

Stringhini, Silvia, et al. 2017. “Socioeconomic Status and the 25×25 Risk Factors as Determinants of Premature Mortality: A Multicohort Study and Meta-Analysis of 1.7 Million Men and Women.” The Lancet . Published online January 31, 2017. doi:10.1016/S0140-6736(16)32380-7.

Tourangeau, K., C. Nord, T. Lê, A.G. Sorongon, and M. Najarian. 2009. Early Childhood Longitudinal Study, Kindergarten Class of 1998–99 (ECLS-K): Combined User’s Manual for the ECLS-K Eighth-Grade and K–8 Full Sample Data Files and Electronic Codebooks (NCES 2009-004) . U.S. Department of Education. Washington, D.C.: National Center for Education Statistics.

Tourangeau, K., C. Nord, T. Lê, A.G. Sorongon, M.C. Hagedorn, P. Daly, and M. Najarian. 2013. Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K:2011), User’s Manual for the ECLS-K:2011 Kindergarten Data File and Electronic Codebook (NCES 2013-061). U.S. Department of Education. Washington, D.C.: National Center for Education Statistics.

Tourangeau, K., C. Nord, T. Lê, K. Wallner-Allen, M.C. Hagedorn, J. Leggitt, and M. Najarian. 2015. Early Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K:2011), User’s Manual for the ECLS-K:2011 Kindergarten–First Grade Data File and Electronic Codebook, Public Version (NCES 2015-078) . U.S. Department of Education. Washington, D.C.: National Center for Education Statistics.

Tourangeau, K., C. Nord, T. Lê, K. Wallner-Allen, N. Vaden-Kiernan, L. Blaker, and M. Najarian. 2017. Early Childhood Longitudinal Study, Kindergarten Class of 2010–11 (ECLS-K:2011) User’s Manual for the ECLS-K:2011 Kindergarten–Second Grade Data File and Electronic Codebook, Public Version (NCES 2017-285) . U.S. Department of Education. Washington, D.C.: National Center for Education Statistics.

U.S. Department of Education (U.S. ED). 2015. A Matter of Equity: Preschool in America .

U.S. Department of Health and Human Services (U.S. HHS) and U.S. Department of Education (U.S. ED). 2016. Policy Statement to Support the Alignment of Health and Early Learning Systems .

Van Voorhis, F.L., M.F. Maier, J.L. Epstein, C.M. Lloyd, and T. Leung. 2013. The Impact of Family Involvement on the Education of Children Ages 3 to 8: A Focus on Literacy and Math Achievement Outcomes and Social-Emotional Skills . MDRC.

Waldfogel, Jane. 2006. “What Do Children Need?” Public Policy Research vol. 13, no. 1, 26–34.

Weiss, Elaine. 2016a. Bright Futures in Joplin, Missouri . A Broader, Bolder Approach to Education.

Weiss, Elaine. 2016b. Vancouver Public Schools (Vancouver, WA) . A Broader, Bolder Approach to Education.

Weiss, Elaine. 2016c. Partners for Education at Berea College, Berea, Kentucky . A Broader, Bolder Approach to Education.

Weiss, Elaine. 2016d. Northside Achievement Zone (North Minneapolis, MN) . A Broader, Bolder Approach to Education.

Weiss, Elaine. 2016e. East Durham Children’s Initiative (East Durham, NC). A Broader, Bolder Approach to Education.

Weiss, Elaine. 2016f. Bright Futures (Pea Ridge, AR) . A Broader, Bolder Approach to Education.

Weiss, Elaine. 2016g. City Connects (Boston, MA) . A Broader, Bolder Approach to Education.

Weiss, Elaine. 2016h. The Children’s Aid Society Community Schools (New York, NY) . A Broader, Bolder Approach to Education.

Weiss, Elaine. 2016i. A Broader, Bolder Education Policy Framework . A Broader, Bolder Approach to Education.

Wentzel, Kathryn R., Shannon Russell, and Sandra Baker. 2016. “Emotional Support and Expectations from Parents, Teachers, and Peers Predict Adolescent Competence at School.” Journal of Educational Psychology vol. 108, no. 2, 242–255.

Yamamoto, Yoko, and Susan D. Holloway. 2010. “Parental Expectations and Children’s Academic Performance in Sociocultural Context.” Educational Psychology Review vol. 22, no. 3, 189–214. doi:10.1007/s10648-010-9121-z.

Appendix A. Data

Introduction.

Our research benefits from the existence of two companion studies conducted by the National Center for Education Statistics (NCES), the Early Childhood Longitudinal Study of the Kindergarten Class of 1998–1999 and the Early Childhood Longitudinal Study of the Kindergarten Class of 2010–2011 (hereafter, ECLS-K 1998–1999 and ECLS-K 2010–2011). The data from these studies come with multiple advantages and a few disadvantages.

The studies follow two nationally representative samples of children starting in their kindergarten year and continuing through their elementary school years (eighth grade for 1998–1999 cohort and fifth grade for the 2010–2011 cohort). The tracking of students over time is one of the most valuable features of the data. The studies include assessments of the children’s cognitive performance and knowledge as well as skills that belong in the category of noncognitive, or social and emotional, skills. The studies also include information on teachers and schools (provided by teachers and administrators) and interviews with parents.

Another valuable feature of the data is the availability of two ECLS-K studies (ECLS-K 1998–1999 and ECLS-K 2010–2011), which allows for cross-comparisons “of two nationally representative kindergarten classes experiencing different policy, educational, and demographic environments” (Tourangeau et al. 2013). The two studies are 12 years apart, or a full school cycle apart: when the 2010–2011 kindergarten class was starting school, the 1998–1999 class was starting the grade leading to their graduation. A comparison of the studies thus offers insightful information about the consequences of changes in the system that may have occurred during an entire cohort’s school life. For the 2010 study, the sample included 18,174 children in 968 schools. i The 1998 study sample included 21,409 children in 903 schools. ii

This existence of data from two cohorts is also a limitation to the current study, as explained by Tourangeau et al. (2013), who note that the assessment scores for the 2010–2011 class are not directly comparable with those developed for the class of 1998–1999. Although the IRT (Item Response Theory) procedures used in the analysis of data were similar across the two studies, each study incorporated different items, which means that the resulting scales are different. Tourangeau et al. (2013) state that “a subsequent release of the ECLS-K: 2010–2011 data will include IRT scores that are comparable with the ECLS-K 1998 cohort.” Up to the point of publication of the current study, this information had not yet been released, and we use standardized scores, instead of raw scores, for the outcomes examined. We can assess changes in the relative position in a distribution (i.e., how far apart high- and low-SES children are in 1998 and how far apart high- and low-SES children are in 2010), but not overall changes in their performance (i.e., it is not possible to ascertain whether performance has improved overall, or if gaps are smaller or larger due to an improvement in performance of children at the low end (specifically the lowest fifth) of the distribution or due to a decrease in the performance of children at the high end (highest fifth) of the distribution, etc.). A full comparison remains to be produced, upon data availability.

We use data for the first wave of each study, corresponding with fall kindergarten (or school entry).

For the analyses, we use the by-year standardized scores corresponding to the fall semester. (The 1998 IRT scale scores for reading and mathematics achievement and assessments of noncognitive skills are standardized using the 1998 distribution and its mean and sd; for 2010, we use the mean and sd of the 2010 distribution.)

Cognitive skills

Cognitive skills are assessed with instruments that measure each child’s:

Reading skills: print familiarity, letter recognition, beginning and ending sounds, rhyming words, word recognition, vocabulary knowledge, and reading comprehension
Math skills: conceptual knowledge, procedural knowledge, and problem-solving; number sense, properties, and operations; measurement; geometry and spatial sense; data analysis, statistics, and probability; and patterns, algebra, and functions

Principal noncognitive skills

We use the term “principal” to identify a set of noncognitive skills that are measured by both the ECLS-K 1998–1999 and 2010–2011 surveys, and that have been relatively extensively used in research.

Teachers are asked to assess each child’s:

Self-control: ability to control behavior by respecting the property rights of others, controlling temper, accepting peer ideas for group activities, and responding appropriately to pressure from peers
Approaches to learning: organizational skills (keeps belongings organized); curiosity (is eager to learn new things); independence (works independently); adaptability (easily adapts to changes in routine); persistence in completing tasks; focus (ability to pay attention); and ability to follow classroom rules

Parents are asked to assess their child’s:

Self-control: ability to control behavior by refraining from fighting, arguing, throwing tantrums, and getting angry
Approaches to learning: persistence (keeps working at something until finished); curiosity (shows interest in a variety of things); focus (concentrates on a task and ignores distractions); helpfulness (helps with chores); intellectual curiosity (is eager to learn new things); and creativity (in work and play)

For the analyses, we use the following set of covariates. The definitions, and the coding used for the covariates, by year, are shown in Appendix Table A1 .

Appendix B. Methodology

Gaps by socioeconomic status.

The expressions below show the specifications used to estimate the socioeconomic status–based (SES-based) performance gaps. For any achievement outcome A , we estimate four models:

Model 1 shows the unadjusted (descriptive) differences for children belonging to different racial/ethnic groups or SES quintiles (the reference group is children in the lowest SES quintile, “low SES”).
Model 2 adjusts for school clustering of students in different schools (i.e., gaps of students in the same schools). The purpose of this clustering is to account for school segregation (i.e., concentration of children of the same race, socioeconomic status, etc., in schools, which causes the raw average performance of students to differ from the adjusted-by-clustering average). It offers a comparison of the gaps shown by peer students in the same schools and classrooms (García 2015; Magnuson and Duncan 2016 offer these estimates as well).

These estimates build on all the available observations (i.e., only those children who have missing values in the outcome variables are eliminated from the analysis).

Because of lack of response in some of the covariates used as predictors of performance, we construct a common sample with observations with no missing information in any of the variables of interest (see information about missing data for each variable in Appendix Table C1 ). We estimate two more models: iii

Model 3 shows gaps adjusted for child and family characteristics, prekindergarten care arrangements, number of books the child has, and early literacy practices at home iv
Finally, Model 4 shows the fully adjusted differences (adjusted for child and family characteristics, prekindergarten care arrangements, early literacy practices at home, number of books the child has, and parental expectations)

The equation below shows the equation we estimate for Models 1 through 4.

$A_{i, s}^{c,nc}= \delta_{o}+\delta_{1}SES2_{i,s} +\delta_{2}SES3_{i,s}+\delta_{3}SES4_{i,s}+\delta_{4}SES5_{i,s} +\delta_{5}Year2010_{i,s}+\delta_{6}Year2010xSES2_{i,s}+\delta_{7}Year2010xSES3_{i,s}+\delta_{7}Year2010xSES4_{i,s}+\delta_{8}Year2010xSES5_{i,s}+Controls+\alpha_{s}+\epsilon_{i,s}$

Appendix C. Sensitivity analysis (I): Multiple imputation

Following standard approaches in this field, we use multiple imputation to impute missing values in both the independent and dependent variables, for the analysis of skills gaps and changes in them from 1998 to 2010 by socioeconomic status (main analysis). See share of missing data by variable in Appendix Table C1 . We use the mi commands in Stata 14, using chained equations, which jointly model all functional terms. The number of iterations was set up equal to 20. Imputation is performed by year.

Our functional form of the imputation model is specified using SES, gender, race, disability, age, type of family, number of books, educational activities, and parental expectations, as well as the original cognitive and noncognitive variables, as variables to be imputed. We use various specifications, combining different sets of auxiliary variables, mi impute methods, and other parameters, to capture any sensitivity of the results to the characteristics of the model. For example, income, family size, and ELL status are set as auxiliary variables and used in several of the imputation models. Another imputation option that was altered across models is the use of weights, as we ran out of imputation models using weights and not using them.

In the imputation model, in order to impute categorical variables’ missingness, we use the option augment, to prevent the large number of categorical variables to be imputed from causing problems of perfect prediction (StataCorp. 2015). The rest of the variables are first imputed as continuous variables. In a second exercise, we also impute SES and educational expectations as ordinal variables (also using the option augment).

In order to calculate the standardized dependent variables, we use the variables derived from the imputation variables (also known as passive imputation). This “fills in only the underlying imputation variables and computes the respective functional terms from the imputed variables” (StataCorp. 2015). In one case, we imputed the dependent variables directly as continuous variables (though we anticipated that the distribution of the scores imputed this way would not necessarily have a mean of 0 and a standard deviation of 1).

Using the imputed data, we estimate Models 1 through 4 following the specifications explained above (from no regressors to fully specified models).

The main findings of our analysis are not sensitive to missing data imputation. The estimates of the gaps in 1998 and the changes in the gaps from 1998 to 2010 are consistent across models in terms of statistical significance. There are some minor changes in the sizes of the estimated coefficients, especially those associated with the changes in the gaps (though all are statistically not different from 0, as discussed in the report using the results from the analysis with the complete cases). There are also some minor changes in the standard errors, though they are small enough to widen the coefficients’ statistical bandwidth to not include the 0.

Appendix D. Sensitivity analysis (II): The different scores available in ECLS-K and the sensitivity of the results to changing them

Children’s reading and mathematics skills are measured using several different metrics in ECLS-K. Among these, the best-known or more commonly used metrics in research are the IRT-based theta scores and the IRT-based scale scores (IRT stands for Item Response Theory). NCES provides data users with definitions of these metrics and recommendations on how to appropriately choose among the different metrics. NCES explains that both theta and IRT-based scale scores are valid indicators of ability. This makes them suitable for research purposes, even though each is expressed in its own unit of measurement. NCES recommends that analysts “consider the nature of their research questions, the type of statistical analysis to be conducted, the population of interest, and the audience” when choosing the appropriate score for analysis (see Tourangeau et al. 2013).

Although nothing would indicate that this could be the case, our work noted that results of analyses such as the one developed in this study are in some ways sensitive to the metrics used as dependent variables. v Thus, the purpose of this appendix is to illustrate the differences in the results associated with different analytic decisions in terms of the metrics used. As we will see, in essence, point estimates depend on the metric used, but the results do not change in a meaningful way and conclusions and implications remain unchanged. That is, although caution is required when interpreting the results obtained using different combinations of metrics, procedures (including standardization), and data waves, it is important to state that the main conclusions of this study— that social-class gaps in cognitive and noncognitive skills are large and have persisted over time — hold . So do the policy recommendations derived from those findings: sufficient, integrated, and sustained over-time efforts to tackle early gaps in a more effective manner.

The scores: Which one to use and definitions

NCES makes the following recommendations for researchers who are choosing among scales (see Tourangeau et al. 2013): vi

When choosing scores to use in analysis, researchers should consider the nature of their research questions, the type of statistical analysis to be conducted, the population of interest, and the audience. […] The IRT-based scale scores […] are overall measures of achievement. They are appropriate for both cross-sectional and longitudinal analyses. They are useful in examining differences in overall achievement among subgroups of children in a given data collection round or in different rounds, as well as in analysis looking at correlations between achievement and child, family, and school characteristics. […] Results expressed in terms of scale score points, scale score gains, or an average scale score may be more easily interpretable by a wider audience than results based on the theta scores. The IRT-based theta scores are overall measures of ability. They are appropriate for both cross-sectional and longitudinal analyses. They are useful in examining differences in overall achievement among subgroups of children in a given data collection round or across rounds, as well as in analysis looking at correlations between achievement and child, family, and school characteristics. […] The theta scores may be more desirable than the scale scores for use in a multivariate analysis because generally their distribution tends to be more normal than the distribution of the scale scores. However, for a broader audience of readers unfamiliar with IRT modeling techniques, the metric of the theta scores (from -6 to 6) may be less readily interpretable. […]

The two scores are defined as follows (see Tourangeau et al. 2013, section “3.1 Direct Cognitive Assessment: Reading, Mathematics, Science”):

The IRT-based scale score is an estimate of the number of items a child would have answered correctly in each data collection round if he or she had been administered all of the questions for that domain that were included in the kindergarten and first-grade assessments. To calculate the IRT-based overall scale score for each domain, a child’s theta is used to predict a probability for each assessment item that the child would have gotten that item correct. Then, the probabilities for all the items fielded as part of the domain in every round are summed to create the overall scale score. Because the computed scale scores are sums of probabilities, the scores are not integers. The IRT-based theta score is an estimate of a child’s ability in a particular domain (e.g., reading, mathematics, science, or SERS) based on his or her performance on the items he or she was actually administered. […] The theta scores are reported on a metric ranging from -6 to 6, with lower scores indicating lower ability and higher scores indicating higher ability. Theta scores tend to be normally distributed because they represent a child’s latent ability and are not dependent on the difficulty of the items included within a specific test.

Reardon (2007) describes the calculation of the theta scores in the following manner: vii

For each test [math and reading], a three-parameter IRT model was used to estimate each student’s latent ability…at each wave…. The IRT model assumes that each student’s probability of answering a given test item correctly is a function of the student’s ability and the characteristics [discrimination, difficulty, and guessability] of the item…. Given the pattern of students’ responses to the items on the test that they are given, the IRT model provides estimates of both the person-specific latent abilities at each wave… and the item parameters. (Reardon 2007, 10) viii

He also notes that “[b]ecause the ECLS-K tests contain many more ‘difficult’ items than ‘easy’ items, the relationship between theta and scale scores is not linear (a unit difference in theta corresponds to a larger difference in scale scores at theta=1 than at theta=-1, for example). The scale scores are difficult to interpret as an interval-scale metric (or are an interval-scaled metric only with respect to the specific set of items on the ECLS-K tests),” while he shows that the “theta scores are interval-scale metrics, in a behaviorally-meaningful sense” (Reardon 2007, 11, 13). ix

The analyses

For the analyses, both the scale and the theta scores need to be standardized by year (the original variables are not directly comparable because they rely on different instruments, as explained by NCES, and the resulting standardized variables have mean 0 and standard deviation 1). This is a common practice in the education field, as it allows researchers to use data that come from different studies and would not have a common scale otherwise. We need to take into consideration that the underlying units of measurement for each variable are different, but after standardization, the metrics are common, expressed in standard deviations and represent the population’s distribution of abilities.

The distributions of the scale and theta scores are shown in Appendix Figures D1 and D2 . In each figure, the plots reflect a more normally distributed pattern for the theta scores (right panel) than for the scale scores (left panel). The companion table, Appendix Table D1 , shows the range of variation for the four outcomes (mean and standard deviations are 0 and 1 as per construction).

We next offer a comparison of the results obtained when using the scale scores versus using the theta scores ( Appendix Table D2 ). We highlight the following main similarities and differences between the results obtained using the scale scores and the results using the theta scores.

Gaps are all equally statistically significant and persistent.
For example, looking at the unadjusted estimates in reading, the gap in 1998 between high- and low-SES children is 1.071 sd if using the scale scores and 1.233 sd if using the theta scores. In math, the gap between high- and low-SES children in 1998 is 1.258 sd if using the scale scores and 1.330 sd if using the theta scores.
Looking at the adjusted estimates in reading, the 1998 gap between high- and low-SES children is 0.596 sd if using the scale scores and 0.684 sd if using the theta scores. In math, the gap between high- and low-SES children is 0.610 sd if using the scale scores and 0.632 sd if using the theta scores.
For example, looking at the unadjusted estimates in reading, the change in the gap between 1998 and 2010 for high- and low-SES children is 0.098 sd if using the scale scores and -0.052 sd (not statistically significant) if using the theta scores. In math, the change in the gap between high- and low-SES children is -0.008 sd (not statistically significant) if using the scale scores and -0.078 sd if using the theta scores.

In Appendix Table D3 , we compare the results obtained using the different scales and the different proxies of socioeconomic status (our composite SES index, mother’s education, number of books, and household income).

Gaps are larger, as mentioned above, when we use the theta scores than when we use the scale scores.
Among the four social-class proxies, the largest gaps are associated with mother’s education, and the smallest gaps are associated with number of books. All are statistically significant.
Looking at the unadjusted gaps, we note that trends are the same (and similar in size) if income is used as the proxy. For mother’s education, the change in the gap between 1998 and 2010 is -0.020 sd in reading (not statistically significant) and -0.154 sd in math if using the scale scores and -0.135 sd in reading and -0.218 sd in math if using the theta scores.
With respect to the adjusted gaps, changes in the gaps are larger when using the theta scores both for household income and mother’s education as indicators of social class. Using the theta scores, the gaps in reading and math shrank over time, while using the scale scores, the only significant reduction was in math when mother’s education was the social class proxy.

Other considerations

There are two other significant pieces of information affecting the cognitive scores in more recent documentation released by NCES. In 2015, NCES announced in its ECLS-K User’s Manual that a

change in methodology required a re-calibration and re-reporting of the kindergarten reading scores since the release of the base-year file. Therefore, the kindergarten reading theta scores included in the K-1 data file are calculated differently than the previously released kindergarten theta scores and replace the kindergarten reading theta scores included in the base-year data file. The modeling approach stayed the same for mathematics and science, so the recalculation of kindergarten mathematics and science theta scores was not needed. (Tourangeau et al. 2015)

Following up on this, the most recent (2017) data user’s manual explains that

The method used to compute the theta scores allows for the calculation of theta for a given round that will not change based on later administrations of the assessments (which is not true for the scale scores, as described in the next section). Therefore, for any given child, the kindergarten, first-grade, and second-grade theta scores provided in subsequent data files will be the same as theta scores released in earlier data files , with one exception: the reading thetas provided in the base-year data file . After the kindergarten-year data collection, the methodology used to calibrate and compute reading scores changed; therefore, the reading thetas reported in the base-year file are not the same as the kindergarten reading thetas provided in the files with later-round data [emphasis added]. Any analysis involving kindergarten reading theta scores and reading theta scores from later rounds, for example an analysis looking at growth in reading knowledge and skills between the spring of kindergarten and the spring of first grade, should use the kindergarten reading theta scores from a data file released after the base year. The reading theta scores released in the kindergarten-year data file are appropriate for analyses involving only the kindergarten round data; analyses conducted with only data released in the base-year file are not incorrect, since those analyses do not compare kindergarten scores to scores in later rounds that were computed differently. However, now that the recomputed kindergarten theta scores are available in the kindergarten through first-grade and kindergarten through second-grade data files, it is recommended that researchers conduct any new analyses with the recomputed kindergarten reading theta scores. For more information on the methods used to calculate theta scores, see the ECLS-K: 2011 First-Grade and Second-Grade Psychometric Report (Najarian et al. forthcoming). (Tourangeau et al. 2017)

Therefore, because of these changes in NCES methodology and reporting, and in light of the comparisons in this appendix, one could expect additional slight changes in the estimates using the IRT-theta scores for reading for kindergarten if using rounds of data posterior to the first round (and probably if using the IRT-scale scores as well, as these values are derived from the theta scores), relative to the first data file of ECLS-K: 2010-2011 released by NCES in 2013. We would not necessarily expect, though, any changes when using the standardized transformation of those scores, because NCES’s documentation does not mention changes to the distribution of the scores, only to their values. We will explore these issues further upon the release of the scores that are comparable across the two ECLS-K studies without any transformation.

Appendix E. Descriptions of 12 community-level whole-child education initiatives

Initiatives that serve part of a school district, austin, texas.

The needs of children in Austin Independent School District (AISD) schools with the highest concentrations of poor, immigrant, and non-English-speaking families are supported through a combination of parent-organizing (schools with parent-organizing programs, led by the nonprofit Austin Interfaith, form a network of “Alliance Schools”), intensive embedding of social and emotional learning (SEL) in all aspects of school policy and practice, and the transformation of schools into “community schools” (i.e., schools that are hubs for the provision of academic, health, and social services).

Organizing partners: Austin Interfaith (a nonprofit of congregations, public schools, and unions that is part of the national Industrial Areas Foundation [IAF]); the Collaborative for Academic, Social and Emotional Learning (CASEL); the American Federation of Teachers (AFT); and the National Education Association (NEA).
Schools and students reached: The IAF/Alliance Schools network extended at its zenith into one-fourth of AISD elementary schools and one-half of AISD high-poverty elementary schools. CASEL worked in five high schools, and in the seven middle schools and 43 elementary schools that feed into these high schools, to embed social and emotional learning in school policies and practices. A middle school and a high school have been transformed into community schools and serve as the models for planned districtwide expansion of the “community schools” strategy into all AISD schools.
General makeup of the student body: In the district overall, 60 percent of students qualify for subsidized meals, i.e., are eligible for free or reduced-price lunch (FRPL); 28 percent are English language learners (ELL); and 10 percent are special education students. In schools targeted for whole-child supports, relative to the general student body, students are poorer, more heavily minority and immigrant, and more likely to be living in single-parent households.
Key features: Parent-organizing with teachers in Alliance Schools enables parents to partner with teachers to advocate for comprehensive supports for their children. Also, social and emotional learning (SEL) is embedded in all aspects of school efforts in the high schools and the feeder elementary and middle schools that worked with CASEL. Finally, health and other wraparound supports in high-needs middle and high schools, along with other community schools features, are expanding to additional district schools.
Core funding: The district received a CASEL grant to embed social and emotional learning in school policies and practices, and also received in-kind support from the NoVo Foundation in the form of technical assistance. The United Way of Greater Austin provides funds for wraparound support, and AFT and NEA fund community schools work and expansion.

Boston, Massachusetts

The City Connects program provides targeted academic, social, emotional, and health supports to every child in 20 of the city’s schools with the highest shares of low-income, black, Hispanic, and immigrant students.

Organizing partners: Boston College Center for Optimized Student Support, Boston Public Schools (BPS), and community agencies.
Schools and students reached: The 20 BPS schools in the program serve more than 8,000 of the city’s most disadvantaged students (out of 125 BPS schools and 56,000 students).
General makeup of the student body: The 20 urban schools serve neighborhoods that are poor and racially and ethnically diverse, with a heavy concentration of Hispanic English-language learners. Over 80 percent of the students in these schools are FRPL-eligible and roughly half do not speak English at home.
Key features: School site coordinators in each school connect students with a tailored set of services and enrichment opportunities provided by a variety of public and private agencies. Universal state health care supports all students’ physical and mental health needs, and the city’s Universal Pre-Kindergarten (UPK) program now offers quality pre-K for all four-year-olds in Boston.
Core funding: In addition to school district budget revenue, federal Race to the Top funds allocated to City Connects help defray costs. Several private foundations support various aspects of City Connects’ work.

Durham, North Carolina

The East Durham Children’s Initiative (EDCI) concentrates services and supports for the children and their families living in a 120-block, heavily distressed area of concentrated poverty and high crime within the city.

Organizing partners: Community leaders launched EDCI and engaged the Duke University Center for Child and Family Health to grow capacity. EDCI is now a fully staffed nonprofit that runs the initiative.
Schools and students reached: The 120-block area targeted by EDCI serves students in two neighborhood elementary schools, one middle school, one high school, and two charter schools.
General makeup of the student body: The 120-block area is urban and poor with a predominantly black but very diverse student body. In Durham schools overall, 66 percent of students are FRPL-eligible, nearly half are black, almost one-third are Hispanic, and 18 percent are white.
Key features: EDCI is a place-based initiative modeled on the Harlem Children’s Zone, providing a pipeline of high-quality cradle-to-college-or-career services. These include early childhood supports (that complement state pre-K programs), health and mental health services, and after-school and summer enrichment activities.
Core funding: EDCI has an annual fund receiving contributions from individuals, corporations, fundraising events, and private foundations; it neither seeks nor receives public funding.

Minneapolis, Minnesota

The Northside Achievement Zone (NAZ) is a Promise Neighborhood, a designation awarded by the U.S. Department of Education Promise Neighborhoods program to some of the most distressed neighborhoods in the nation. Through the program, children and families who live in the 13-by-18 block NAZ receive individualized supports.

Organizing partners: NAZ, the Promise Neighborhood grantee organization, is guided by a 20-member board of directors consisting of local leaders.
Schools and students reached: The 13-by-18 block zone in North Minneapolis serves 5,500 students in 10 public, charter, and parochial K–12 schools, including one high school.
General makeup of the student body: In this racially concentrated area of poverty, almost all residents are African American, and median family income is $18,000. One-third of children are homeless or “highly mobile” (not technically homeless but without stable housing).
Key features: “Connectors” are in essence case managers who help families develop achievement plans, and “Navigators” connect families with community resources to move toward goals. The zone offers access to high-quality pre-K and parenting supports, as well as mentoring, enrichment, college preparatory support, and after-school and summer programs.
Core funding: NAZ is anchored by a federal Promise Neighborhood grant. NAZ also receives private grants and is able to leverage federal Race to the Top Early Learning Challenge funds to support pre-K scholarship slots.

New York, New York

Through a collaboration between The Children’s Aid Society and the New York City Department of Education, 16 community schools in some of the most disadvantaged neighborhoods in three of the city’s five boroughs provide wraparound health, nutrition, mental health, and other services to students along with enriching in-and-out-of-school experiences, amplified by extensive parental and community engagement.

Organizing partners: The Children’s Aid Society, the New York City Department of Education, the New York State Education Department, and other local and state agencies.
Schools and students reached: Sixteen community schools in three boroughs serve some of the poorest immigrant and minority students in a school system of roughly one million students.
General makeup of the student body: Students in Children’s Aid Society community schools are disadvantaged relative to the system overall, which serves a heavily low-income and minority student body: more than three quarters of New York City public school students are FRPL-eligible, 13 percent are English language learners, and nearly one in five receive special education services. These schools also have high concentrations of students of color: 27 percent are African American and 41 percent are Hispanic.
Key features: Close coordination with local and state education, health, and other agencies along with community partnerships at each school enables wraparound health, mental health, and after-school and summer enrichment, as well as deep parental and community engagement.
Core funding: A range of public dollars, including federal Elementary and Secondary Education Act (ESEA) Title I funds and funds from the federal 21st Century Community Learning Centers program, together with state and local funding for after-school and other programs, is supplemented by funds from individuals and foundations.

Orange County, Florida

The Tangelo Park Project (TPP) provides cradle-to-college support for all children residing in Orlando’s high-poverty, heavily African American Tangelo Park neighborhood.

Organizing partners: The Tangelo Park Program board, along with Harris Rosen (the hotelier who envisioned and funds the program), work in close collaboration with the Tangelo Park Civic Association and the University of Central Florida.
Schools and students reached: The program serves all children in the Tangelo Park neighborhood.
General makeup of the student body: Virtually all residents in the low-income neighborhood are African American or Afro-Caribbean.
Key features: Universal college scholarships—called “Promise” scholarships because they are guaranteed by an established fund—are supported by quality neighborhood-based early childhood education, health, counseling, and after-school and summer programs.
Core funding: Harris Rosen funds early child care providers and universal college scholarships. Rosen also supports other services, such as a lifeguard at the YMCA, as needed.

Initiatives that serve all of a school district

Joplin, missouri.

Joplin’s Bright Futures initiative (which has spawned dozens of other Bright Futures affiliate districts under a Bright Futures USA umbrella since it launched in 2010) has a rapid response component that addresses children’s basic needs (within 24 hours of a need being reported), while strong school–community partnerships help meet students’ longer-term needs. Bright Futures also provides meaningful service learning opportunities in every school.

Organizing partners: The Joplin School District’s superintendent and top leadership, in collaboration with parents and community, faith, business, and social service leaders.
Schools and students reached: Bright Futures serves all of the district’s 7,874 students in all 17 schools.
General makeup of the student body: Joplin is a heavily white community. As of 2015, nearly two-thirds (61 percent) of Joplin students are FRPL-eligible and 16 percent are classified as needing special education; just 3 percent are English language learners.
Key features: The Bright Futures USA framework has three components. First, a rapid response system is designed to meet any student’s basic health, nutrition, or physical need within 24 hours of such a need being reported; this system is supported by combined resources from social service agencies, businesses, faith organizations, and individual community members. Second, school- and community-level councils build community leadership and partnerships with schools to meet longer-term needs and sustain systems. Third, service learning opportunities are embedded in all schools to help develop children as citizens. Teachers lead the service learning and receive training to do so. In addition to these three components, Joplin also provides pre-K for at-risk students, as well as tutoring, mentoring, and after-school and college preparatory programs based on student need.
Core funding: Federally funded Americorps VISTA volunteers provide in-kind support; funds from the state departments of Elementary and Secondary Education and of Economic Development support Bright Futures work and conferences; and the regional Economic Security Corporation and a range of private funders supplement these federal and state funding sources.

Kalamazoo, Michigan

The “Kalamazoo Promise,” a guarantee by a group of anonymous local philanthropists to provide full college scholarships in perpetuity for graduates of the district’s public high schools brought Kalamazoo Public Schools (KPS), the city, and the community together to develop a set of comprehensive supports that enable more students to use the scholarships.

Organizing partners: Kalamazoo Promise and Kalamazoo Public Schools, the local school district, in collaboration with Communities in Schools Kalamazoo (CIS) and other nonprofit entities.
Schools and students reached: All KPS students (12,216 in 25 schools) who graduate from Kalamazoo public high schools are eligible for Promise scholarships. CIS works in all schools but to varying degrees and with varying levels of financial support.
General makeup of the student body: In this combination urban–suburban district, a large majority of students (over 70 percent) are FRPL-eligible, 12 percent receive special education services, and 7 percent are English language learners. The share of African American students grew from less than one-third in 1987 to over half 30 years later; over this period the share of Hispanic students increased as well.
Key features: The anchor for comprehensive supports is universal “Promise” college scholarships, which have spurred community leadership to provide quality pre-K programs and wraparound health, mental health, and other supports, and to launch a districtwide effort to create a college-going culture and resources to support that culture.
Core funding: Anonymous donors have committed to funding Promise scholarships in perpetuity. CIS is supported by a combination of Title I funding, which helps support school coordinators; 21st Century Learning grants for after-school activities; and private individual and philanthropic donations.

Montgomery County, Maryland

All students in Montgomery County Public Schools (MCPS) benefit from zoning laws that advance integration and strong union–district collaboration on an enriching, equity-oriented curriculum. These efforts are bolstered by extra funding and wraparound supports for high-needs schools and communities.

Organizing partners: MCPS, Montgomery County Education Association (the local teachers union), Montgomery County Council, and Linkages to Learning (a joint initiative of MCPS and the county council that provides an integrated focus on health, social services, community development, and engagement to support student learning, strong families, and healthy communities.)
Schools and students reached: All 160,000 students in more than 200 schools are served via some services. Higher-poverty schools and their communities receive additional funds and supports that are broader and more intensive. For example, Linkages to Learning serves more than 5,400 individuals—students and their family members—per year at 29 schools. Over 3,700 of them receive comprehensive behavioral health or social wraparound services to mitigate the effects of poverty and reduce nonacademic barriers to learning.
General makeup of student body: The MCPS school district as a whole is racially and socioeconomically diverse: 30 percent of students are Hispanic, 29 percent are white, 22 percent are African American, 14 percent are Asian, and 35 percent are FRPL-eligible (more than 40 percent of students have been FRPL-eligible at some point). On the poorer, Eastern side of the county, where more intensive whole-child supports are provided, the 10 highest-poverty schools have student bodies that are at least 80 percent FRPL-eligible.
Key features: Mixed-use housing policies that enable racial and socioeconomic integration advance school-level integration that boosts low-income students’ learning, which the district enhances through various forms of support, including high-quality early childhood education, parent and community outreach, reallocation of funds to high-needs schools and students, nutrition and health services, and an emphasis on social and emotional learning.
Core funding: MCPS is heavily locally funded, with almost no federal Title I dollars. The district’s whole-child approach draws on a combination of school district and county revenues, along with federal funding for Head Start programs, state pre-K dollars, and assorted other grants.

Pea Ridge, Arkansas

The Pea Ridge School District, a small suburban–rural district outside Fayetteville, Arkansas, is among the newer affiliates of Bright Futures USA, a national umbrella group that grew out of Bright Futures Joplin. As a Bright Futures affiliate, Pea Ridge is making good progress toward identifying and meeting students’ basic needs, engaging the community to meet longer-term needs, and making service learning a core component of school policy and practice.

Organizing partners: Pea Ridge School District and Bright Futures USA.
Schools and students reached: Eight hundred and fifty students are served in one primary school, one elementary school, one middle school, and one high school, as well as an alternative high school and a new career-tech charter high school.
General makeup of the student body: The suburban–rural district is mostly white, with a small but growing Hispanic population, and predominantly middle-income with pockets of both higher-income families and families in poverty.
Key features: The first component of the three-part Bright Futures USA framework is a rapid response system to meet every student’s basic health, nutrition, and physical needs within 24 hours through a combination of social service agency, business, faith, and individual community contributions. Other components include school- and community-level councils, which build community leadership and partnerships with schools to meet longer-term needs and sustain systems, and service learning embedded in all schools that is enhanced by supportive training for teachers. Pea Ridge also provides pre-K for at-risk students, as well as tutoring, mentoring, and after-school and college preparatory programs for students who need them.
Core funding: State funds support meals and other needs for high-poverty schools, and Pea Ridge has secured a three-year private grant to support access to pre-K for low-income students.

Vancouver, Washington

Family and Community Resource Centers (FCRCs) currently serve 16 of the highest-needs Vancouver Public Schools (VPS) district schools, with mobile and lighter-touch support in other schools and plans to expand districtwide by 2020.

Organizing partners: School district leaders coordinate the program with the support of six central-office staff (three of whom just support FCRCs). Technical and other assistance is provided by the Coalition for Community Schools.
Schools and students reached: FCRCs serve 23,500 students in 16 VPS schools: 11 elementary schools, two middle schools, two high schools, and the Fruit Valley Learning Center (a combination elementary school and community center that also offers child care and Head Start programs). Plans are being made to expand FCRCs to all 35 VPS schools by 2020.
General makeup of the student body: As of 2015, more than half of students were FRPL-eligible, with FRPL-eligibility rates in some central-city schools exceeding 80 percent. More than one in five students speak a language other than English at home and 12.5 percent of students are special education students; in FCRC schools, the shares of non–English speakers and special education students are even higher.
Key services: VPS supports a range of early childhood education programs, including quality pre-K; middle and high school in-school enrichment; after-school and summer programs (provided by VPS partners); and help for parents and families through workshops, assistance, and referrals to a range of community resources.
Core funding: District and Title I funds, which support basic FCRC needs, are supplemented by cash and in-kind donations from faith-based, social service, business, and association partners.

Initiative that serves multiple school districts

Eastern (appalachian) kentucky.

A federal Promise Neighborhood grant helps Berea College’s Partners for Education provide intensive supports for students and their families in four counties in the Eastern (Appalachian) region of Kentucky and provide lighter-touch supports in an additional 23 surrounding counties. (Berea College, which was established in 1855 by abolitionist education advocates, is unique among U.S. higher-education institutions. It admits only economically disadvantaged, academically promising students, most of whom are the first in their families to obtain postsecondary education, and it charges no tuition, so every student admitted can afford to enroll and graduates debt-free.)

Organizing partners: Berea College launched Partners for Education (PfE), which is now a fully staffed nonprofit that runs the initiative.
Schools and students reached: PfE serves 35,000 students in 22 schools in Clay, Jackson, Knox, and Owsley counties; tens of thousands more are served less intensively in an additional 23 counties in the region.
General makeup of the student body: The Appalachian region is rural, very poor, and heavily white. The regional poverty rate is around 27 percent (in 2015), and reaches as high as 40 percent in some counties. About 80 percent of students are FRPL-eligible and 97 percent are white.
Key features: Family engagement specialists meet directly with families and help coordinate services provided by a range of community partners. Other specialists provide basic academic, college preparatory, and health and other wraparound services to students.
Core funding: Federal Promise Neighborhood, Full Service Community Schools, and Investing in Innovation grants are the most prominent sources of funding, but the initiative receives a range of other cash and in-kind supports.

Appendix tables and figures

Covariates from these models : ecls-k 1998--1999 and 2010--2011.

Source: ECLS-K, kindergarten classes of 1998–1999 and 2010–2011 (National Center for Education Statistics)

Missing data

	1998	2010
Variable	Percent missing	Percent missing
Race/ethnicity
White	0.2	0.5
Black	0.2	0.5
Hispanic	0.2	0.5
Hispanic English language learner (ELL)	6.6	11.8
Hispanic English speaker	6.6	11.8
Asian	0.2	0.5
Others	0.2	0.5
Socioeconomic status	5.9	11.9
Family composition: Not living with two parents	15.5	26.3
Mother’s education	7.5	42.8
Pre-K care, center-based	16.8	17.4
“Literacy/reading activities” index	15.6	26.4
“Other activities” index	15.6	26.5
Parents’ expectations for children’s educational attainment	16.1	26.5
Number of books	16.3	26.7
Outcomes
Reading	17.7	13.8
Math	13.0	14.2
Self-control (by teachers)	13.8	25.4
Approaches to learning (by teachers)	10.4	18.7
Self-control (by parents)	15.8	27.3
Approaches to learning (by parents)	15.8	27.3

Note: For detailed information about the construction of these variables, see Appendix Table A1.

Distribution of standardized scale and theta scores in mathematics, by year

Scale scores, 1998 (left) and 2010 (right).

Theta scores, 1998 (left) and 2010 (right)

Distribution of standardized scale and theta scores in reading, by year

Descriptive statistics of standardized scale and theta scores, by year (not weighted)

	1998				2010
	N	(Mean, sd)	Min	Max	N	(Mean, sd)	Min	Max
Scale score–reading	17,620	(0,1)	-1.39	10.13	15,670	(0,1)	-2.4	4.06
Theta score–reading	17,620	(0,1)	-2.72	4.30	15,670	(0,1)	-3.47	5.01
Scale score–math	18,640	(0,1)	-1.69	9.86	15,600	(0,1)	-2.22	4.23
Theta score–math	18,640	(0,1)	-3.13	4.48	15,600	(0,1)	-5.78	6.28

Note: N is rounded to the nearest multiple of 10.

Reading and math skills gaps between high-SES and low-SES children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010, using scale and theta scores as dependent variables

	Model 1 (unadjusted)				Model 4 (fully adjusted)
	Full sample				Restricted sample
	Scale scores		Theta scores		Scale scores		Theta scores
	Reading	Math	Reading	Math	Reading	Math	Reading	Math
Gap in 1998	1.071***	1.258***	1.233***	1.330***	0.596***	0.610***	0.684***	0.632***
	(0.024)	(0.022)	(0.024)	(0.022)	(0.031)	(0.031)	(0.032)	(0.031)
Change in gap by 2010	0.098***	-0.008	-0.052	-0.078**	0.080	0.051	-0.016	-0.002
	(0.033)	(0.032)	(0.033)	(0.032)	(0.052)	(0.048)	(0.054)	(0.050)
N	30,950	31,850	30,950	31,850	26,050	26,890	26,050	26,890
Adj.R2	0.152	0.189	0.170	0.197	0.293	0.336	0.336	0.353

Notes: Standard errors are in the parentheses. N is rounded to the nearest multiple of 10. Asterisks denote statistical significance: *** p < 0.01, ** p < 0.05, * p < 0.1.

Source: ECLS-K, kindergarten classes of 1998-1999 and 2010–2011 (National Center for Education Statistics)

Reading and math skills gaps between high-social class and low-social class children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010, using scale and theta scores as dependent variables

		Model 1 (unadjusted)				Model 4 (fully adjusted)
		Full sample				Restricted sample
		Scale scores		Theta scores		Scale scores		Theta scores
		Reading	Math	Reading	Math	Reading	Math	Reading	Math
By SES	Gap in 1998	1.071***	1.258***	1.233***	1.330***	0.596***	0.610***	0.684***	0.632***
		(0.024)	(0.022)	(0.024)	(0.022)	(0.031)	(0.031)	(0.032)	(0.031)
	Change in gap by 2010	0.098***	-0.008	-0.052	-0.078**	0.080	0.051	-0.016	-0.002
		(0.033)	(0.032)	(0.033)	(0.032)	(0.052)	(0.048)	(0.054)	(0.050)
By mother’s education	Gap in 1998	1.294***	1.457***	1.412***	1.502***	0.696***	0.681***	0.739***	0.685***
		(0.038)	(0.036)	(0.038)	(0.035)	(0.058)	(0.050)	(0.048)	(0.044)
	Change in gap by 2010	-0.020	-0.154***	-0.135***	-0.218***	-0.075	-0.119*	-0.135*	-0.182***
		(0.051)	(0.049)	(0.051)	(0.048)	(0.082)	(0.070)	(0.075)	(0.067)
By number of books	Gap in 1998	0.736***	0.966***	0.847***	1.032***	0.347***	0.424***	0.388***	0.438***
		(0.028)	(0.027)	(0.028)	(0.026)	(0.034)	(0.031)	(0.033)	(0.031)
	Change in gap by 2010	0.083**	-0.019	-0.015	-0.088**	-0.540***	-0.818***	-0.594***	-0.829***
		(0.039)	(0.038)	(0.039)	(0.038)	(0.184)	(0.188)	(0.181)	(0.174)
By household income	Gap in 1998	1.090***	1.308***	1.214***	1.320***	0.384***	0.443***	0.429***	0.439***
		(0.042)	(0.041)	(0.042)	(0.041)	(0.058)	(0.060)	(0.049)	(0.050)
	Change in gap by 2010	-0.127**	-0.230***	-0.247***	-0.292***	-0.006	-0.060	-0.058	-0.099
		(0.060)	(0.059)	(0.060)	(0.059)	(0.084)	(0.082)	(0.076)	(0.072)

Notes: Standard errors are in parentheses. Asterisks denote statistical significance: *** p < 0.01, ** p < 0.05, * p < 0.1.

Endnotes to the appendices

i. The sample design used to select the individuals in the study was a three-stage process that involved using primary sampling units and schools with probabilities proportional to the number of children and the selection of a fixed number of children per school. In the last stage, children enrolled in kindergarten or ungraded schools were selected within each sampled school. A clustered design was used to limit the number of geographic areas and to minimize the number of schools and the costs of the study (Tourangeau et al. 2013, 4-1).

ii. The dataset in the first year followed a stratified design structure (Ready 2010, 274), in which the primary sampling units were geographic areas consisting of counties or groups of counties. About 1,000 schools — 903 for 1998 and 968 for 2010—were selected, and about 24 children per school were surveyed. Assessment of the children was performed by trained evaluators, while parents were surveyed over the telephone. Teachers and school administrators completed the questionnaires in their schools.

iii. As a sensitivity check, we estimate Models 1 and 2 using Models 1’s and Model 2’s specifications but using the restricted sample (these results are not shown here, but are available upon request).

iv. As a sensitivity check, we estimate Model 3 parsimoniously, by including family characteristics only, and then adding family investments (prekindergarten care arrangements, early literacy practices at home, and number of books the child has), and then adding parental expectations (with and without interactions with time); results of the sensitivity check are not shown, but are available upon request).

v. We refer to the fact that we are using the same data and that the scale and theta scores are based on the same instruments and are not independent from each other. Advice on this possibility is found in Reardon (2007), who cites work by Murnane et al. (2006) and Selzer, Frank, and Bryk (1994) that also warn about this option.

vi. From NCES: “IRT uses the pattern of right and wrong responses to the items actually administered in an assessment and the difficulty, discriminating ability, and guess-ability of each item to estimate each child’s ability on the same continuous scale. IRT has several advantages over raw number-right scoring. By using the overall pattern of right and wrong responses and the characteristics of each item to estimate ability, IRT can adjust for the possibility of a low-ability child guessing several difficult items correctly. If answers on several easy items are wrong, the probability of a correct answer on a difficult item would be quite low. Omitted items are also less likely to cause distortion of scores, as long as enough items have been answered to establish a consistent pattern of right and wrong answers. Unlike raw number-right scoring, which treats omitted items as if they had been answered incorrectly, IRT procedures use the pattern of responses to estimate the probability of a child providing a correct response for each assessment question” (Tourangeau et al. 2017, 3-2).

vii. The quoted text is abridged to remove variables and formulas specific to Reardon’s study and not central here.

viii. Also, “the estimated scale score is the estimated number of questions the student would have gotten correct if he or she had been asked all of the items on the test. The estimated scale score is obtained by summing the predicted probabilities of a correct response over all items, given the student’s estimated theta score and the estimated item parameters” (Reardon 2007, 11).

ix. They are equally spaced units along the scale without a predefined zero point.

See more work by Emma García and Elaine Weiss

COVID-19 and education: An emerging K-shaped recovery

Across the nation this fall, school bells once more summoned students back to classrooms that, in some districts, had stood empty for the previous year and a half. According to Burbio, a data service that aggregates school and community calendars, 100 percent of public-school students are attending schools in districts that offer fully in-person instruction this fall compared with 73 percent last spring. Meanwhile, 91 percent of students taking interim assessments through educational-technology company Curriculum Associates this fall did so in school buildings, up from just 64 percent in the spring and 34 percent the previous fall. This research therefore provides the first opportunity to truly understand the impact of the pandemic on learning for all students (see sidebar, “About the research”). 1 Previous assessments could provide accurate data only for students who took them in person, because changing the assessment environment (for example, taking the test at home) has a meaningful impact on results. Students who take tests at home tend to perform better than those who take tests in a classroom. Previous analyses thus looked only at students who had returned to classrooms and did not include those who were fully remote for instruction and testing.

About the research

To gauge the impact of the pandemic on educational achievement, we analyzed four main data sets: Curriculum Associates’ i-Ready assessment data to understand student academic performance before and during the pandemic, Burbio’s K-12 district report to explore disruptions to learning, Burbio’s Elementary and Secondary School Emergency Relief (ESSER) III Spending Tracker to delve into district spending plans, and our proprietary parent survey to understand the perceptions and experiences of parents through the pandemic. We supplemented this with a broader review of academic research, publications of state and district assessment results, and other news stories.

We use the term unfinished learning to denote the difference between where students would have been if the pandemic had not occurred and where students actually are today. This reflects the reality that many students did not have the opportunity to learn as much during the past 20 months as they would have done typically. While some students may have actually slipped backward and “lost” learning, most students continued to progress during the pandemic, but at a slower pace than they would have done otherwise.

Curriculum Associates’ i-Ready assessments were taken by 6.9 million students in math and 6.1 million students in reading in the fall of 2021. Our analysis was based upon a sample of 3.0 million students in mathematics and 2.7 million students in reading who took the diagnostic assessments in school buildings, selected to meet historical comparison criteria. The math sample covered 50 states and Washington, DC, and the reading sample covered 49 states and Washington, DC. Florida was overweighted across both samples, accounting for 22 percent of the math sample and 30 percent of the reading sample. We analyzed data for grades one through six to be consistent with the spring analysis.

When calculating unfinished learning, it is not possible to determine the exact amount that students fell behind or recovered in each time period (from fall 2020 to spring 2021 to fall 2021). The subset of students in each assessment window was significantly different, because more students took in-person assessments in 2021 than in 2020. The fall 2020 numbers relied on 0.2 million in-school students, the spring 2021 numbers relied on 1.3 million in-school students, and the fall 2021 numbers rely upon 2.7 million in-school students.

We cannot isolate the impact of a changing subset versus that of actual changes in learning. However, we can say that the fall 2021 numbers are the most accurate reflection of where students are today—being the most recent data and encompassing the largest and most representative set of students.

To calculate the amount of unfinished learning, we compared fall 2021 results with those of students from matched schools from fall in 2017, 2018, and 2019. We then converted the points difference into months of learning by comparing the historical scores from the fall of one grade level with performance in the fall of the next grade level, treating this fall-to-fall variation in historical scores as one “year” of learning. Our analysis assumed each year consisted of ten months of school and a two-month summer vacation. Actual school schedules vary significantly, and i-Ready’s typical growth expectations are based upon 30 weeks of actual instruction from the fall to the spring rather than a comparison of fall-to-fall academic growth.

We further analyzed the differential impact of the pandemic on different student groups. In the Curriculum Associates i-Ready assessment data, majority-Black schools are those in which more than 75 percent of the student population identifies as Black; majority-White schools are those in which more than 75 percent of the student population identifies as White. Across all our analyses, high income is defined as family income of $75,000 or more, while low income is defined as family income of less than $25,000.

Burbio is a data service that aggregates school and community calendars. Burbio’s K-12 district report includes two different periods: spring data (as of May 28, 2021) and fall data (as of November 25, 2021). Burbio audits nearly 5,000 school districts, representing more than 75 percent of US student enrollment. 1 For more on Burbio’s methodology, please see “Burbio’s Methodology,” burbio.com.

Burbio’s Elementary and Secondary School Emergency Relief (ESSER) III Spending Tracker tabulates planned spending for more than 1,400 districts in 44 states. We analyzed committed spending for districts that broke down spending into different categories. These districts have committed the funds to particular purposes, but the funds may not have been disbursed and programs may not have commenced. Our analysis does not account for districts that have earmarked funds for categories but not indicated specific dollar amounts.

Finally, we augmented these data sets with proprietary research on the perceptions of parents regarding their children’s learning experiences and needs. McKinsey conducted two surveys: a survey of 16,370 parents between June 1 and June 21, 2021; and a survey of 14,498 parents that took place from October 29 to November 14, 2021. Respondents were parents of children in kindergarten through 12th grade across all 50 states and Washington, DC. We then compared the experiences of students by location, race and ethnicity, and income levels.

Our analysis finds that students remain behind in both math and reading. What’s more, gains made since the spring are uneven. While some students are making up lost ground, others are stagnating. For example, students in majority-Black schools remain five months behind their historical levels in both mathematics and reading, while students in majority-White schools are now just two months behind their historical levels, widening prepandemic achievement gaps. 2 Based on Curriculum Associates i-Ready fall 2021 assessment data. See Understanding student learning: Insights from fall 2021 , Curriculum Associates, November 2021, curriculumassociates.com. This means that, in math, students in majority-Black schools are now 12 months behind their peers in majority-White schools, having started the pandemic nine months behind. Similarly, concerns around student mental health have lessened somewhat since the spring, but they remain higher than before the pandemic.

Furthermore, we aren’t even out of the woods yet. Disruptions to learning are not over, and student attendance rates lag significantly behind prepandemic levels. While actual closures of whole schools or districts have affected just 9 percent of students, quarantines and other disruptions have affected 17 percent of in-person students. On top of school closures, absenteeism rates have risen, with 2.7 times as many students on a path to be chronically absent from school this year compared with before the pandemic. While absenteeism rates for high-income students are leveling off, rates for low-income students have continued to worsen since the spring, despite the return to in-person school. If historical correlations between chronic absenteeism and high school graduation hold, this could translate into an additional 1.7 million to 3.3 million eighth–12th graders dropping out of school because of the pandemic. 3 The federal definition of chronic absenteeism is a student who misses more than 15 days of school each year. The Utah Education Policy Center’s research brief on chronic absenteeism calculates the overall correlation between one year of chronic absence from eighth to 12th grade and dropping out of school is 0.134. For more, see “Research brief: Chronic absenteeism,” Utah Education Policy Center, July 2012, uepc.utah.edu. Our analysis then examined the differential in chronic absenteeism between fully virtual and fully in-person students to account for virtual students reengaging when in-person education is offered. For students who had stopped attending school, we assumed 50 to 75 percent would not return to learning. This estimation is based partly on the UChicago Consortium on School Research’s on-track indicator as a predictor of high school graduation, which estimates up to 75 percent of high school students who are “off track”—either failing or behind in credits—do not graduate in five years. For more, see Elaine Allensworth and John Q. Easton, “The on-track indicator as a predictor of high school graduation,” UChicago Consortium on School Research, June 2005, consortium.uchicago.edu.

Varying access to support programs could be another reason why some students seem to be bouncing back more quickly than others. More than $200 billion has been allocated by the federal government to K-12 schooling, but just a small portion of that has reached students thus far, and parents report uneven access to benefits. High-income parents are 21 percentage points more likely to report their child has participated in a program to support either academic or mental-health recovery, including tutoring, summer school, after-school programs, and counseling and mentoring. 4 Forty percent of low-income parents and 61 percent of high-income parents indicated that their child participated in at least one of these programs. If current trends persist, students from high-income families could recover unfinished learning by the end of this school year. Historically disadvantaged students, meanwhile, could remain up to a grade level behind their peers. 5 This assumes that from fall 2021 to spring 2022 students catch up twice as many months of unfinished learning as they did between spring 2021 and fall 2021. It also factors in prepandemic achievement gaps between high-income and low-income schools.

As districts plan for further recovery programs, an understanding of which students most need support can help inform decisions and begin to close both preexisting and new opportunity and achievement gaps.

How students are doing today

A few months into this school year, students appear to be performing better academically and feeling better than they were last spring. However, indicators of both academic performance and broader well-being are still well below prepandemic levels.

The amount of unfinished learning varies significantly across schools

Our sample of the Curriculum Associates i-Ready assessment data, which covers nearly three million students across 50 states, 6 Over six million students took the reading and math Curriculum Associates diagnostic assessments in the fall of 2021, but only about three million students met historical comparison sample inclusion criteria and took the assessment in school. suggests students are four months behind in mathematics and three months behind in reading compared with students in matched schools in previous years. This level of unfinished learning is about a month less (that is, better) compared with the spring, suggesting students may be making up some of the academic ground they lost during the pandemic.

However, a deeper look at the data reveals the amount of unfinished learning is far from equitable. Students in majority-Black schools are five months behind where they would otherwise have been, both in math and reading. Students in majority-White schools are now just two months behind historical levels (Exhibit 1).

Inequalities were already baked into the historical levels: even before the pandemic, students in majority-Black schools were nine months behind students in majority-White schools in mathematics in the Curriculum Associates data set. Students in majority-Black schools are now a full 12 months behind those in majority-White schools, widening the preexisting achievement gap between Black and White students by about a third (Exhibit 2).

Within classrooms, teachers face a wider range of student needs than ever

The differences identified in our analysis are likely even greater at the student level. In the United States, the variation among students within a school is typically three times greater than the variation among schools. 7 Programme for International Student Assessment (PISA) 2012 results: Excellence through equity: Giving every student the chance to succeed (volume II) , OECD, 2013, oecd.org; US between-school versus within-school data. This fall, each classroom likely included students with a broad range of experiences during the past year and a half. Where students stand compared to grade-level expectations reveals this disparity. 8 Understanding student learning: Insights from fall 2021 , Curriculum Associates, November 2021, curriculumassociates.com. In math, for example, the share of students at grade level or above has decreased by six percentage points since the pandemic, while the share of students who are two or more grade levels below has increased by nine percentage points. 9 Averages quoted for grades one through six to be consistent with the previous “months of learning” analysis from spring 2021. The exhibit provides data from kindergarten through eighth grade. However, readers should bear in mind that i-Ready is used only for a subset of students in some middle schools. This pattern means that in a math classroom of 30 fourth-grade students, for example, three additional students are now two or more grade levels below (Exhibit 3). This makes an already difficult job tougher, as teachers need to tailor their instruction to an even broader set of student needs.

This is most concerning for early-elementary students in reading and mid- to late-elementary students in math. Students who do not learn to read proficiently by third grade struggle to “read to learn” thereafter and are four times less likely to graduate high school. 10 Double jeopardy: How third grade reading skills and poverty influence high school graduation , Annie E Casey Foundation, January 2012, aecf.org. Similarly, students who do not master middle-grade math concepts such as fractions and whole-number division will likely struggle in more conceptual high school mathematics and are less likely to graduate high school on time. 11 Robert Siegler et al., “Early predictors of high school mathematics achievement,” Psychological Science , 2012, Volume 23, Number 7, pp. 691–7; Robert Balfanz, Liza Herzog, and Douglas J. Mac Iver, “Preventing student disengagement and keeping students on the graduation path in urban middle-grades schools: Early identification and effective interventions,” Educational Psychologist , 2007, Volume 42, Number 4, pp. 223–35.

High school students have also fallen behind

We drew on state assessments to understand the pandemic’s impact on older students. A subset of states administered assessments in spring 2021, but many had low participation rates, making the results difficult to compare. Where data are available, the story is sobering. For the 13 states 12 Arizona, Iowa, Louisiana, Massachusetts, Missouri, Mississippi, North Carolina, North Dakota, Oklahoma, South Dakota, Tennessee, West Virginia, and Wyoming. with participation rates higher than 90 percent, the proportion of students meeting proficiency standards dropped by an average of five percentage points in math and three percentage points in English language arts. These declines are similar in magnitude to the reduction in students achieving grade-level proficiency in the Curriculum Associates data set, 13 Per Curriculum Associates data, 17 percent of students in fall 2021 were on or above grade level in mathematics compared with 23 percent of the historically matched population (a decrease of six percentage points). For reading, 28 percent were on or above grade level in fall 2021 as opposed to 31 percent historically (a decrease of three percentage points). These percentage-point declines are similar in magnitude to the percentage-point declines in high school assessment proficiency rates. suggesting the pandemic may be having an equal impact on high school learning. Furthermore, these results likely underestimate unfinished learning in high school, as the states and students experiencing the longest disruption are not reflected in the assessments.

The pandemic’s impact goes beyond academics

Parents remain concerned about their children’s academic performance, school attendance, and mental health. This level of concern has dropped since the spring but remains higher than before the pandemic. In our survey, for example, 21 percent of parents reported being very or extremely concerned about their child’s mental health before the pandemic. This share spiked to 35 percent in June 2021 and dropped back to 28 percent this November. Parents of Black and Hispanic students have higher levels of concern across all areas (Exhibit 4).

Across the board, the picture a few months into the fall semester looks marginally better, on average, than it did in the spring, but the top-line numbers hide a lot of variability. Upon delving deeper, many students—especially those from historically disadvantaged backgrounds—still need help.

Ongoing disruptions to learning

Before states and districts can fully help students recover from the pandemic, disruptions to learning must end. Burbio reports 1,282 school closures since the start of the 2021–22 school year. 14 Burbio data as of November 25, 2021; includes closures due to school mental-health days. That means 9 percent of public-school students have been affected by a school closure, with the average closure lasting two days. 15 Average closure duration is calculated by the number of closure days weighted by the number of students disrupted. For example, shorter closures that affected more students are given a greater weight than longer closures that affected fewer students. However, in ten states, more than 15 percent of students have endured closures (Exhibit 5).

Our parent survey paints an even less rosy picture (Exhibit 6). Of all students who chose to attend fully in-person learning this fall, 16 Excluding students whose parents said they had chosen to put their child partially or fully into virtual, hybrid, or home-based learning in the past two weeks. Of our total sample, 6 percent of parents chose to homeschool their child, 5 percent selected fully virtual learning, and 19 percent opted for some form of hybrid model. just 83 percent attended ten full days during the two weeks the survey was in the field. 17 Numerator reflects students who opted for and attended in-person learning for all days in the past two weeks that weren’t holiday days, minimum days, or absences due to sickness or other non-COVID-19 disruption reason. Parents of Black and Hispanic students were slightly more likely to report disruptions to their children’s in-person learning experience.

It is not just the duration of disruption that matters; the alternative to in-person learning is also critical. In the case of school closures, Burbio data suggest students received some form of virtual instruction 54 percent of the time when disruptions occurred. In some states, that number was much lower: in Arkansas, Iowa, Maryland, Missouri, Tennessee, and Texas, students received instruction during less than 10 percent of school closures.

Our parent survey also indicates a wide variety of options when in-person school is disrupted. Of all disrupted days (excluding holidays), alternatives provided by districts included synchronous virtual (54 percent), asynchronous virtual (30 percent), and no instruction at all (15 percent). 18 Synchronous virtual includes videoconferencing and direct-teacher instruction, and asynchronous virtual encompasses activities such as independent work packets. Parent satisfaction with the quality of learning options provided also varied significantly. While 75 percent of parents had high or very high confidence in the quality of fully in-person education, that total dropped to 64 percent for parents whose children experienced disruptions to learning.

Reducing the number of disruptions requires an understanding of what is causing them. The Burbio data suggest that confirmed or suspected COVID-19 cases in staff, students, or the local community account for only 12 percent of closure days. 19 Excluding all closures for which no reason was recorded (n = 788 of total 1,282 closures). These percentages have been weighted by student days disrupted to account for closure duration and number of students affected. Pandemic-related stressors are causing most of the disruptions: 50 percent of closure days are the result of primarily single-day breaks that school districts have taken to support student and staff mental health. Staff shortages also make up a significant portion (13 percent) of closure days. For individual students, our parent survey suggests campus closures account for only 45 percent of total missed days, with quarantines making up 12 percent and sickness an additional 6 percent. 20 For parents whose students had signed up for in-person learning (did not choose virtual or hybrid) but missed one or more days of regular scheduled instruction of the past two weeks. Parents of Black and Hispanic students were most likely to report that campus closures were the cause of their students’ interruptions to in-person learning.

Even if school is open, students don’t always attend. Accounts of rising absenteeism have emerged in recent months. Nearly half of Cleveland’s students are on track to be chronically absent this school year. 21 Patrick O’Donnell, “Schools are open, but Cleveland kids keep cutting class: Chronic absenteeism is more than double pre-pandemic levels,” The 74 , October 26, 2021, the74million.org. An analysis of 30 districts in California, representing more than 330,000 students, shows rates of chronic absenteeism have more than doubled since the pandemic started. 22 Data from School Innovations & Achievement suggest that chronic absenteeism from a subset of districts in California rose from 11 percent in 2019 to 18 percent in 2020 to 27 percent in 2021, with the largest increases in the primary grades. See Statewide chronic absenteeism analysis , School Innovations & Achievement, 2021, sia-us.com.

Our parent survey suggests these news stories are not isolated anecdotes. According to respondents, just over half of students attended school every day this fall. Many students are missing multiple days of school. Before the pandemic, about 8 percent of parents reported that their children were chronically absent from school (missing 15 days or more of school during the year 23 There are multiple definitions of chronic absenteeism. In this article, we use the federal definition, which counts a student who has missed 15 days of school for any reason during one school year as chronically absent. Some states, including California, instead categorize students as chronically absent if they miss 10 percent or more of instructional days. ). In the spring, chronic absenteeism more than doubled, to 18 percent. This fall, those numbers increased again, with 22 percent of parents now reporting their child is on track to be chronically absent this school year. 24 Twenty-two percent of parents reported their student had missed four or more days of the 2021–22 school year so far. If these students were to continue missing school at the same rate for the remainder of the school year, they would be on track to miss 15 or more days across the full school year. Put another way, parent reports of chronic absenteeism have increased by a factor of 2.7 since before the pandemic. Low-income students, who often lack access to resources to make up for lost instruction in the classroom and who are more likely to experience ongoing attendance barriers, 25 “Attendance in the early grades: Why it matters for reading,” Attendance Works, February 2014, attendanceworks.org. are 1.6 times more likely to be missing multiple days of school than their high-income peers (Exhibit 7).

Previous research has revealed that parents tend to underestimate their children’s absent days by a factor of two 26 Avi Feller and Todd Rogers, “Reducing student absences at scale by targeting parents’ misbeliefs,” Nature Human Behaviour , April 2018, Volume 2, pp. 335–42, nature.com. The McKinsey Parent Survey, June 2021 (n = 16,370), corroborates Feller and Rogers: the historical US-wide chronic absenteeism rate of 16 percent for grades eight through 12 (per the Department of Education) was two times that of the rates at which parents indicated their student had missed more than 15 days of school per year (“Prior to the pandemic, did your child attend school consistently?”). —suggesting nearly one-third of students across the country may be on track to be chronically absent this school year. Based on historical links between chronic absenteeism and dropout rates, as many as 1.7 million to 3.3 million eighth–12th graders could drop out of school due to the pandemic without coordinated efforts to reengage them in learning. 27 The federal definition of chronic absenteeism is a student who misses more than 15 days of school each year. The Utah Education Policy Center’s research brief on chronic absenteeism calculates the overall correlation between one year of chronic absence from eighth to 12th grade and dropping out of school is 0.134. For more, see “Research brief: Chronic absenteeism,” Utah Education Policy Center, July 2012, uepc.utah.edu. Our analysis then examined the differential in chronic absenteeism between fully virtual and fully in-person students to account for virtual students reengaging when in-person education is offered. For students who had stopped attending school, we assumed 50 to 75 percent would not return to learning. This estimation is based partly on the UChicago Consortium on School Research’s on-track indicator as a predictor of high school graduation, which estimates up to 75 percent of high school students who are “off track”—either failing or behind in credits—do not graduate in five years. For more, see Elaine Allensworth and John Q. Easton, “The on-track indicator as a predictor of high school graduation,” UChicago Consortium on School Research, June 2005, consortium.uchicago.edu.

Efforts to support student recovery

Many school systems around the country are balancing their efforts to continue limiting disruptions while supporting student recovery. The federal government has committed more than $200 billion to K-12 education during the next three years through the Elementary and Secondary School Emergency Relief (ESSER) and Governor’s Emergency Education Relief (GEER) funds, with most of the support going directly to school districts. 28 The Coronavirus Aid, Relief, and Economic Security (CARES) Act of 2020 allocated $13 billion to ESSER and $3 billion to GEER funds; the Coronavirus Response and Relief Supplemental Appropriations Act (CRRSAA) of 2021 provided $54 billion to ESSER II, $4 billion to GEER II, and Emergency Assistance to Non-Public Schools (EANS); and the American Rescue Plan (ARP) Act of 2021 provided $123 billion to ESSER III, $3 billion to GEER (EANS II), and $10 billion to other education programs. For more, see “CCSSO fact sheet: COVID-19 relief funding for K-12 education,” Council of Chief State School Officers, 2021, learning.ccsso.org. It is still too soon to determine whether this funding is being spent on programs that will help students who need it most, but available data provide an overview of existing district commitments and initial parent experience.

Burbio has collected information from 1,420 districts in 44 states on their plans for ESSER III funding (the largest and most recent tranche). Across those districts, 67 percent of funds have been committed thus far. 29 This means districts have decided (committed) how to use the funds, but it does not necessarily mean the funds have been disbursed or that programs have commenced. The denominator excludes districts that have earmarked funds for categories but not indicated specific dollar amounts. Of that amount, 28 percent is focused on academic recovery, with a further 6 percent on mental-health recovery. Within academic recovery, summer school and after-school programs account for 34 percent of funding, while tutoring makes up just 7 percent. 30 Some tutoring programs may not have been coded by districts specifically as “tutoring” but are using existing staff and learning interventions. While tutoring reflects only 7 percent of all committed funds related to academic recovery activities, 34 percent of the 1,420 districts for which Burbio has gathered data have noted at least some funding will go toward tutoring, although many have not yet disclosed a dollar amount. An analysis of committed funds may underestimate the portion of districts planning different acceleration efforts. The Center on Reinventing Public Education (CRPE) reports that 71 percent of the top 100 districts are planning to extend learning, while 62 percent intend to roll out some form of tutoring, and 45 percent want to expand small-group instruction. 31 Bree Dusseault, “By the numbers — how 100 school systems are (and aren’t) recovering from COVID: Tutoring, extra class time & other learning acceleration strategies,” The 74 , November 21, 2021, the74million.org.

Meanwhile, just over half of parents report their students have participated in some form of academic or mental-health recovery program. The largest number of students attended tutoring, homework help, or test-preparation services, followed by academic after-school programs and mental-health and counseling support. Despite the large amount of funds directed to summer school, a smaller portion of students participated in these programs. High-income students are nearly twice as likely to have participated in several of these recovery programs (Exhibit 8).

Students most commonly access these programs through free-of-charge offerings at their schools, with a mix of community organizations and programs paid for by parents making up the balance. Schools provided more than 60 percent of in-person academic summer school and more than 50 percent of in-person tutoring, after-school, and mentoring programs free of charge. Parents were more likely to pay for mental-health or counseling services, with only about 30 percent receiving these offerings from schools.

Our survey suggests unmet demand for some services (Exhibit 9). Across income and ethnicity groups, parents are most interested in in-person tutoring and in-person after-school programs; summer school was at the bottom of the list. In every category, in-person programs are more sought after than virtual ones. Low-income parents are much more likely than high-income parents to say they are not interested in any programs: 33 percent versus 20 percent.

A comparison of the two data sets highlights a possible mismatch between what parents say they want for their children and where districts are currently investing funds. While around 21 percent of ESSER III funds committed for academic recovery are directed toward summer school, 32 Per Burbio, some districts have grouped “summer learning and supplemental after-school programs” in their committed spending. Our analysis assumes that within this category, 50 percent of committed spending is for academic summer school, and 50 percent is for academic after-school programs. only 17 percent of parents are interested in this option. Meanwhile, 7 percent of funds committed for academic recovery are directed to tutoring, yet 29 percent of parents are interested in this option.

In addition, the students who need services most may not be receiving them. High-income students have less unfinished learning, on average, than low-income students. Yet high-income parents are both more concerned about their children’s academic performance and more likely to have signed up their children for programs to help them recover. They are also more satisfied with academic and mental-health recovery programs provided by their children’s school. A more targeted approach may be required to ensure that low-income students and other vulnerable populations are able to access high-quality support and recovery programs.

After an incredibly challenging period, initial research suggests some students are beginning to settle back into their prepandemic school routines. However, prepandemic education was failing many students, and these inequalities have been exacerbated by the pandemic, causing some segments—primarily low-income students and students of color—to fall even further behind their peers. Moreover, disruptions to learning continue, and programs to support students are not always reaching the ones who need it most. If this trend continues, the pandemic could leave students with increasingly unequal access to education and opportunity.

Decades of education research have reinforced the relationship between poverty and depressed learning outcomes, and the income achievement gap has grown over the past three decades as income inequality has risen. 33 Sean F. Reardon et al., Is separate still unequal? New evidence on school segregation and racial academic achievement gaps , Stanford Center for Education Policy Analysis working paper, number 19-06, September 2021, cepa.stanford.edu. An inclusive economic recovery will be important to avoid further exacerbating widening gaps in learning outcomes. School systems are increasingly swimming upstream against these strong educational and economic currents. To not only prevent widening gaps in opportunity and achievement but also close them, systems can invest now to ensure all students have the chance to recover from the pandemic’s many setbacks and reach their full potential.

Emma Dorn is a senior expert in McKinsey’s Silicon Valley office; Bryan Hancock and Jimmy Sarakatsannis are partners in the Washington, DC, office; and Ellen Viruleg is a senior adviser based in Providence, Rhode Island.

The authors wish to thank Annie Chen, Chauncey Holder, and Kunal Kamath for their contributions to this article.

Explore a career with us

COVID-19 and education: The lingering effects of unfinished learning

COVID-19 and learning loss—disparities grow and students need help

COVID-19 and student learning in the United States: The hurt could last a lifetime

Sorry, we did not find any matching results.

We frequently add data and we're interested in what would be useful to people. If you have a specific recommendation, you can reach us at [email protected] .

We are in the process of adding data at the state and local level. Sign up on our mailing list here to be the first to know when it is available.

Search tips:

• Check your spelling

• Try other search terms

• Use fewer words

How uneven educational outcomes begin, and persist, in the US

Long before graduation, factors including early education, household income gaps, and disciplinary actions affect students’ abilities to access resources and succeed in school. These elements impact racial and ethnic groups differently and contribute to these unequal educational outcomes.

Updated on Thu, March 23, 2023 by the USAFacts Team

Highest educational attainment by race or ethnicity

Education remains one of the best predictors of future economic success in the US. In 2019, the median weekly earnings among workers was $1,256 for those with a bachelor’s degree, compared to $746 for those with just a high school diploma and $592 for high school dropouts.

Americans with a college degree weather economic downturns more easily than those without. In June , unemployment among high school graduates without a college degree jumped to 12%, compared to 3.6% the previous year. Among those with a bachelor’s degree and higher though, unemployment increased to 7% (compared to 2.5% last year).

These benefits are not felt equally because educational attainment varies greatly across racial and ethnic lines. According to the US Census Bureau , 31% of Hispanic adults never completed high school, more than double any other racial or ethnic category. Only 26% of Black Americans 25 or older receive a bachelor’s degree or higher, while 40% of non-Hispanic, white students and 58% of Asian students do.

Achievement gaps start in elementary school and continue through high school.

The National Assessment of Educational Progress (NEAP), which administers standardized tests across the country in fourth, eighth, and 12th grades, found disparities in test scores across racial and ethnic categories. As soon as the fourth grade, the average Black student fails to reach basic reading levels and scored 32 points below their average white peers; Hispanic students score, on average, 27 points lower than their white peers.

4th grade NAEP reading scores in 2017

Similar gaps appear in math scores, with Black and Hispanic students scoring 25 and 19 points, respectively, lower than their white peers. Asians students consistently score the highest on both assessments.

4th grade NAEP math scores in 2017

These early differences in test scores hold relatively constant throughout primary and secondary schooling, and remain so over time. From fourth to 12th grade, white students consistently score 25 to 30 points higher than Black students, and 20 to 25 points higher than Hispanic students. These gaps have persisted from 1992 to 2017, the year of the most recent data.

White-Black reading achievement gap (in points)

White-hispanic reading achievement gap (in points), some gaps in educational outcomes between racial and ethnic groups can be connected to income.

Many of these differences in educational outcomes can be attributed to differences in household income. Students in lower-income households lack the resources used by their higher-income peers, such as internet or computer access .

Among students of all racial and ethnic groups, there is a 28 and 24-point gap between fourth-grade reading and math scores, respectively, between students who are eligible for free or reduced lunch and students who are not. The sizes of these gaps are similar to the racial or ethnic gaps in test scores.

4th-grade reading scores and free-lunch eligibility

4th-grade math scores and free-lunch eligibility.

In general, Asian households have the highest median income in the country, followed by white households, Hispanic households, and Black households. Test scores follow this same pattern. However, this pattern is not completely consistent across states and regions. Washington, DC, for instance, has the largest income gap between Black and white households nationwide—a $40,000 gap in median income—but a smaller achievement gap than average.

The income gap between white households and Black or Hispanic households is similar in Virginia and Illinois, but the difference in test scores is much greater in Illinois. Notably, the achievement gap is universal – there isn’t a single state where the average scores of Black or Hispanic students are higher than their white counterparts — even in states with very small income gaps.

Disciplinary actions that take students out of school disproportionately impact male minority students.

Minority students—especially male students—also receive more disciplinary actions than other groups throughout primary and secondary school, which can deter students from graduating high school. In many cases, students who are expelled or referred to law enforcement are a result of schools enforcing zero-tolerance policies. A 2013 Congressional Research Service report states that these policies have not deterred further school violence as districts hoped.

According to the National Center for Education Statistics, nearly 1% of Black and American Indian male students are referred to law enforcement compared to less than 0.5% of Hispanic or white male students. Furthermore, 17% of Black male students receive at least one out of school suspension; the next highest demographic is American Indian male students at 9%.

Percent of students receiving at least one out-of-school suspension

Out-of-school suspensions mean less classroom time, which could discourage and compound challenges for struggling students, and referrals to law enforcement can result in a juvenile record.

Racial and ethnic gaps persist among college students

Disciplinary actions and achievement gaps make it both more difficult for students to complete high school, and may make it more difficult for students to succeed in post-secondary school. While Black students graduate high school at higher rates than Hispanic students, for example, they complete college at lower rates. Only 40% of Black college enrollees graduate from college, compared to 64% of non-Hispanic white students and 74% of Asian students.

Six-year college graduation rate for 2011 cohort

However, racial or ethnic categories are also not monoliths. In 2017, among people who identify as Hispanic, 53% of 18-24 year-old Venezuelans in the US enrolled in college, while 46% of Cubans, 35% of Mexicans, and 28% of Guatemalans did. Among Asians, 76% of Chinese between 18 and 24 years old enrolled in college, while 45% of Cambodians did.

While not all members of the same racial or ethnic group share the same economic or educational characteristics, considering the differences in data among the groups can help understand what may cause educational achievement gaps to exist.

According to the National Assessment of Educational Progress (NAEP), fourth Grade Proficiency in reading is defined as the ability to integrate and interpret texts and apply understanding of the text to draw conclusions and make evaluations. A Basic fourth grade reading level is being able to locate relevant information, make simple inferences, and use their understanding of the text to identify details that support a given interpretation or conclusion. Students should be able to interpret the meaning of a word as it is used in the text.

For math, fourth-grade students performing at the NAEP Basic level should show some evidence of understanding the mathematical concepts and procedures in the NAEP content areas (number properties and operations, measurement and geometry, data analysis and probability, and algebra). Fourth-grade students performing at the NAEP Proficient level should consistently apply integrated procedural knowledge and conceptual understanding to problem solving. They should be able to use whole numbers to estimate, compute, and determine whether results are reasonable. They should have a conceptual understanding of fractions and decimals; be able to solve real-world problems in all NAEP content areas; and use four-function calculators, rulers, and geometric shapes appropriately.

Learn more about education in the US and get the facts every week by signing up for our newsletter .

Explore more of USAFacts

Related articles, nearly two-thirds of preschool-aged children attend early education programs.

In 32 states, teacher salaries have not kept pace with inflation

4.4 million households with children don’t have consistent access to computers for online learning during the pandemic

How many women graduate with STEM degrees?

Related Data

National school lunch participation

30.1 million

Grade 8, Reading: Percent of students at or above proficient

Grade 8, Math: Percent of students at or above proficient

Data delivered to your inbox.

Keep up with the latest data and most popular content.

SIGN UP FOR THE NEWSLETTER

Reference Manager
Simple TEXT file

People also looked at

Systematic review article, educational strategies to reduce the achievement gap: a systematic review.

CIIE—Center for Research and Intervention in Education, Faculty of Psychology and Education Sciences, University of Porto, Porto, Portugal

Despite continuous efforts, the educational achievement gap is still, in most societies, a significant obstacle to ensuring more equity and social justice. Much of this inequality derives from belonging to historically discriminated groups. Indeed, coming from a lower socioeconomic status (SES), of an immigrant, or descendant situation, being Black, Hispanic, Gypsy, or any other racialized condition, still strongly influences academic attainment, school dropout and career choices. However, many innovative strategies and policies have been implemented to minimize this bias. This investigation proposes to gather, assess, and analyze these most recent interventions and perceive which of these present a better level of efficacy. Using the PRISMA guidelines, this Systematic Review of Literature yielded 27 studies that fit the inclusion criteria. The analysis considered the level of efficacy, intervention method and scope. Results show that targeted strategies, such as working on reading abilities and school subjects' focused interventions are more effective in improving minorities' and lower SES students' attainment. Other beneficial initiatives include whole-school, state and community-based projects, innovative pedagogies, and, finally, programs that deal with the psycho-social consequences of racism and discrimination, e.g., the internalization of negative perceptions and expectations. Overall, there is a strong need to develop mixed-method and longitudinal designs that will further our knowledge about what type of measure works, while considering a situated and contextual perspective, instead of a one-size-fits-all approach.

1. Introduction

Extensive research has, time and again, confirmed the existence of continuous and widespread school achievement gaps between groups ( Dietrichson et al., 2017 ; Furgione et al., 2018 ; OECD, 2020 ). Since the Coleman Report ( Coleman et al., 1966 ), there has been critical awareness that schools, despite attempting to promote equality, partially replicate many inequalities arising in societies. Currently, however, social and economic placement in the social structure cannot be considered the only variable that conditions access to and performance in education. In some cases, the racialized condition, for instance, can be a more powerful disadvantage, toward the struggle for equity, within the same economic and social strata. Indeed, belonging to a low socioeconomic level, just like being born to a minority group, being an immigrant or of immigrant descent, may, among other factors, become a predictor of lower educational achievement and higher school dropout ( Sirin, 2005 ; Gonçalves and França, 2008 ; Coimbra and Fontaine, 2015 ; García and Weiss, 2017 ; Burger, 2019 ). These intricate roots make it more challenging to evaluate the foundations of inequalities and generate a sustainable change to this phenomenon.

2. The socioeconomic achievement gap

Historically, groups in disadvantaged economic layers tend to display, on average, a lower academic achievement than their upper lever counterparts. “In both the United States and England, for example, it is estimated that the attainment of high-school students from low-income households lags behind that of their counterparts from higher-income households by the equivalent of more than two and a half years of schooling” ( Easterbrook and Hadden, 2021 , p. 181). Some even contend that the gap has been increasing ( Michelmore and Dynarski, 2017 ). These authors analyzed children and youngsters living in a permanent poverty situation, i.e., students continuously on subsidized school meals, from kindergarten to the 8th grade. They concluded that “These persistently disadvantaged children score nearly one standard deviation below students who were never disadvantaged” (p. 10). Also, these children achieved lower than those in intermittent poverty. In other words, even accounting for other variables, poverty (measured by this proxy of free meals) has an almost direct, negative effect on scores. Unsurprisingly, those who were never eligible for free school meals scored the highest in the 8th-grade exams.

Even at such a precocious age as beginning kindergarten, García and Weiss (2017) have demonstrated notable differences in children's cognitive and non-cognitive skills, between the lowest and highest SES groups, a lag that remained stable from 1998 to 2010.

There is more evidence of this connection. Hung et al. (2020) , for instance, retrieved information from the Stanford Education Archive across five school years (2008–2013), six grade levels (grades third to eighth) and two test subjects (Math and English language). Academic performance was analyzed with an array of sociodemographic and school variables: students receiving free lunch and other SES indicators, students considered English language learners, students receiving special education services, but also the Gini index, and city/urban school district zip code. The results indicated that economic inequality, racial inequality, and household adult education attainment are strongly associated with student achievement gaps.

3. The racial gap

Another long-standing imbalance is the Black/White academic performance gap—similar to the gaps found with other cultural minorities, such as the Roma population, especially in Europe. In recent decades, this situation has become more complex, by accounting for the achievement inequalities between foreigners and the majority population, with a new phenomenon: the increase in immigration movements to Europe and North America. From 2000 to 2020, the United States received 21 640 238 immigrants ( https://www.dhs.gov/sites/default/files/2023-03/2022_1114_plcy_yearbook_immigration_statistics_fy2021_v2_1.pdf ). In Europe, “The number of people residing in an EU Member State with citizenship of a non-member country on the 1st of January 2020 was of 23 million, representing 5.1 % of the EU population” ( https://ec.europa.eu/eurostat/web/education-and-training/overview ). The growth in migration demands adjustments in educational systems to improve integration, language learning, multicultural respect and attention for all these different groups with diverse needs, characteristics and strengths.

Let us begin with the structural, deep-rooted gap between people of color/white persons. For example, despite the many measures and policies implemented in the United States, racial-ethnic differences on tests of school motivation and academic achievement persist ( Mckown, 2013 ; Merolla and Jackson, 2019 ). Also, Hung et al. (2020) identified various indicators that significantly correlated with the achievement gaps between White and African American students across school districts. These included analysis of the percentage of special education students, total expenditure per pupil, average enrolment per grade, city/urban setting, economic inequality between the White and Black, the degree of racial segregation in schools, household unemployment status, and household adult educational attainment. The persistent inequalities may best be explained by these differences in socioeconomic status, family cultural accessibility and opportunities, school type, segregated residential areas, prejudiced academic environments and expectations. Structural racism, therefore, still holds back Americans of color ( Merolla and Jackson, 2019 ).

Some studies support the notion that these gaps have been declining in the last three decades ( Kao and Thompson, 2003 ), not only regarding academic results but also in vocational options (as well as tracking). Nevertheless, gaps remain visible in school accomplishment, graduate studies admission, and completion. These remain higher in African American, Hispanic, and Native American groups than in White and Asian American counterparts ( Kao and Thompson, 2003 ). Part of the racial and ethnic patterns still remain evident, especially in the highest achievement levels. As an example, although Black and Hispanic students are more present in college nowadays, they tend to apply to community college, rather than a 4-year graduation, when compared to their white and Asian colleagues.

Gillborn et al. (2017) also refutes the official positions issued by governmental institutions, which state that the Black Caribbean/White gap is “being eroded” in the United Kingdom. Gillborn contests that, instead, “the odds of greater success for White students remain significant throughout the 25 years we have reviewed, fluctuating between one-and-a-half and more than twice the chance of their Black Caribbean peers” (p. 866) in the UK. Compared to their White peers, the chances for Black Caribbean students to succeed have never improved, considering the level achieved at the end of the last century. The proposed explanation is that those policy interventions intended to set higher standards by increasing the benchmark, have, consequently, actively widened achievement inequities and served to maintain Black Caribbean disadvantage.

In Europe, there has been a wide variability in progress amidst different foreign or cultural minorities and the majority population: “(…) between 1992 and 2004, all racial/ethnic groups experienced an increase in the proportion of pupils obtaining at least five General Certificate of Secondary Education (GCSE) grades of A * to C grades, the national benchmark of achievement in England at the end of compulsory schooling” ( Stevens, 2007 , p. 155). Even more remarkable is that some groups, including the Chinese, Irish, and Indian pupils, now outperform the dominant White group. On the other hand, on average, Bangladeshi, Pakistani and Black pupils still present lower outcomes than the dominant White group. Furthermore, only Indian, and White pupils accomplish continuous year-to-year improvements, whereas other racial/ethnic minority groups show periods during which their success rates fall back ( Stevens, 2007 ).

In the Netherlands, the Dutch government has evaluated its educational policies' intent to integrate immigrant and ethnic minorities, in order to, progressively, level them with the majority population. During the last few decades, the rapid growth in foreign arrivals created new challenges to which the political approaches varied, starting with a multicultural perspective, which guaranteed the preservation of the minorities' culture and language, to a recent, more assimilationist perspective ( Rijkschroeff et al., 2005 ). Findings from this study demonstrate that, although policy and programmatic efforts aim at equalizing educational opportunities, the investments have not yet reduced the disparity.

Another study comparing first and second-immigrant students with national ones in Spain and Italy revealed that the immigrant counterparts underperformed, compared to native students in both countries. Even though socioeconomic level and language skills partly contributed to explaining these achievement gaps, there were still significant differences, after controlling for these variables and family and school characteristics ( Azzolini et al., 2012 ).

Bécares and Priest (2015) analyzed the intersectional contributions of race, ethnicity, gender and class on several features of school attainment, including emotional ones. They concluded that awareness of social stereotypes could often negatively impact one's social group. Considering that, generally, within the same SES group, the gender and race experience entails different socialization processes and unequal access to resources, none of these variables alone can be held accountable for the origin and persistence of the achievement gap. “In general, the largest inequalities in academic outcomes across racial/ethnic and gender groups appeared in the most privileged classes” ( Bécares and Priest, 2015 , p. 8). We might infer, therefore, that the interaction of group characteristics can yield different outcomes for individuals placed at different crossroads, not only in terms of academic performance but also in terms of emotional features.

Educators and politicians trying to deal with these inequalities face a difficult task: how can the performance of low SES groups or minorities improve when the leading cause of their low achievement is the ingrained, prolonged inequality and disadvantage they are embedded in? ( Elias et al., 2013 ). These researchers proved that SES and race still have more influence on school results than other, more mutable variables, like teacher mobility, school, and class size. Moreover, “it is important to note that race/ethnicity, without SES interaction, did not become significant until middle school” ( Elias et al., 2013 , p. 4). The effect of ethnic composition on test scores was higher in schools with a larger reduced or free lunch population. In high school, having a more significant portion of Black or Latino students impacted the educational climate. In sum, in schools with high poverty and high minority it is more challenging to improve test score performance.

As such, different types of inequalities may entwine, reinforce, and sometimes contradict themselves ( Martins et al., 2016 ). Nevertheless, the predominant trend is the systematic accumulation of multiple dimensions of imbalance ( Stiglitz, 2014 ).

Another form of disadvantage lies in access to better professional careers. Increasingly, access to more qualified professions demands a higher level of education. The educational level achieved still strongly affects social mobility regarding professional placement and resource distribution in all European countries ( Martins et al., 2016 ). There is a strong relationship between social class and test scores, educational attainment, and graduate studies attendance and completion (see Lee and Burkam, 2002 ; Duncan et al., 2011 ; Mishel et al., 2012 ; Snellman et al., 2015 ; García and Weiss, 2017 ).

García and Weiss (2017) , comparing data from cohorts in 1998 and 2010, concluded that considerable SES-based gaps in academic performance exist and have persisted at the beginning of kindergarten. SES-based gaps across skills among the 2010 kindergartners have remained the same, compared with the prior academic generation of students ( García and Weiss, 2017 ). Parental activities, parental expectations for their children's attainment, and pre-K participation reduce the gaps between high-SES and low-SES children. However, they are not enough to eliminate these gaps, even considering children's individual and family characteristics. Another crucial finding is that those school districts adopting “whole child” approaches to education are seeing better outcomes for students, from improved readiness for kindergarten to higher test scores and graduation rates, and narrower achievement gaps.

4. Previous systematic reviews on inequality and school achievement

So far, there are have been few systematic reviews produced, on the subject of strategies and measures used for equalizing academic results. Sirin (2005) presents one of these most comprehensive reviews, focusing on the relationship between SES and academic achievement. Seeking to replicate ( White, 1982 ) study on “the relation between socioeconomic status and academic achievement”, Sirin intended first to evaluate the strength of the relation between SES and achievement; next, to uncover possible moderating factors; finally, to bring to light the changes that have been occurring since 1982. The results confirmed that there was a moderate effect size between SES and achievement and a strong effect between school area and achievement, and that this connection has been weakening since White's study. The findings could be a good indicator of the effect of compensatory measures in education or, we add, of improving living conditions.

Berkowitz et al. (2017) produced a critical literature review, integrating a comprehensive collection of studies dating back to 2000, examining whether a positive school and class climate can successfully interfere with the association between low SES and poor academic achievement. “In conclusion, findings from the current study demonstrate the overall positive contribution of positive climate to academic achievement among all students but especially those from lower SES backgrounds” ( Berkowitz et al., 2017 , p. 459).

A more comprehensive systematic review and meta-analysis, closer to our goals in this paper, is the work by Dietrichson et al. (2017) , which focused on “effective academic interventions for elementary and middle school students with low socioeconomic status” (p. 1). They suggest an array of possibilities, from parent training programs to health interventions, role model interventions, and early childhood intervention programs, as possible means to increase the academic achievement of children with low SES. Such interventions may act on primary areas of development such as cognitive development, social adjustment, family support, motivational support, increased expectations, and pedagogical support ( Reynolds et al., 2010 ). Dietrichson's team gathered interventions implemented by schools, researchers, and local stakeholders and studies that used a treatment-control design. They concluded that there is a positive impact for low SES students, in elementary and middle education, with interventions such as tutoring, feedback and progress monitoring, and cooperative learning.

A very recent review by Ashraf et al. (2021) evaluated how interventions targeted at Free Meal student beneficiaries have contributed to improving their attainment. Through a meta-analysis of existing trials, they analyzed several interventions with similar pedagogical characteristics, like, for example, one-to-one tuition, compared to whole-class teaching methods. The analysis shows mixed results: the interventions had, indeed, positive effects on the literacy outcomes of the participating children; however, none of the 48 experiments had the same impact on mathematics. Despite the evidence of improvements, these were equivalent in both Free Meal and Non-Free Meal students, meaning the gap between them remained.

Nevertheless, our goals in the current paper go a step further in the revision of studies implicating this relationship between SES and school achievement. Considering the ever-mutating flow of newcomers to western (and other) countries, it seems crucial to pay special attention to the case of cultural minorities and foreigners, via immigration or refugee status.

As such, the main objectives of the current study are: (a) to gather, in a systematic manner, recent literature on the impact of educational interventions designed to reduce achievement inequalities; (b) to organize and categorize the most relevant studies concerning SES and immigrant/minority condition as baseline variables; c) to integrate and summarize the differential effects of these interventions on the targeted populations.

5. Methodology

This systematic search and analysis of the literature used the Preferred Reporting Items for Systematic Review and meta-analysis protocols PRISMA-P ( Moher et al., 2015 ). The PRISMA Guidelines assert that a clear and detailed protocol of the procedures should be the starting point from which to assemble and organize the relevant studies that match previously established criteria.

The reason for the choice of a Systematic Review instead of a Meta Analysis, which was a relevant alternative possibility, relies on the vast array of interventions and programs we encountered in our initial search. It would be a limitation to consider all these actions as single, comparable variables. We decided not to restrain the interventions and the methodology and allow this study to uncover all the variability of interventions, in all forms.

By adopting the PRISMA protocol, we defined the following criteria for including studies. First, the studies should define the level of the school population. We decided to concentrate specifically on elementary, middle, and secondary school levels, on the grounds of having some form of academic evaluation available in these segments. Second, interventions should aim at reducing inequalities in academic outcomes based on social, economic, or cultural features. Third, studies must include some level of efficacy measure. In the case of qualitative studies, the analysis must confirm whether there are any noticeable changes, as stated by the participants; for quantitative studies, quasi-experimental or experimental studies should have academic performance as the dependent variable.

In this review, it is essential to consider what kind of interventions, projects, and even political measures are effective in transforming educational outcomes, to what extent and in which groups are affected, and which ones are not. It is also important to consider why some function better than others, if this reflection is available. The time frame considered here was between 2000 and 2021.

In sum, the research questions we explore in this review are the following:

1) What kind of interventions or projects aimed at reducing the SES and minorities achievement gap can be found in studies from 2000 until 2021?

2) What is the measured and/or perceived efficacy of each of these interventions?

The search for our review involved the following databases: Academic Search Ultimate, ERIC, Education Source, APA PsycINFO, Psychology and Behavioral Sciences Collection, Sociology Source Ultimate, and PsychArticles. Later, a second search was directed to the Journal of Research on Education Effectiveness.

The keywords used were the following: educational achievement or academic achievement AND program or intervention assessment AND racial or minority AND socioeconomic status or socioeconomic status. The restrictions for the search included peer-reviewed, full text and academic journals only.

This search was conducted in English, Spanish and Portuguese using the exact keywords in the same EBSCO databases, and also Scielo.

5.1. Exclusion criteria

• Studies on the origins of inequalities, without any intervention to reduce them.

• Studies that do not include underprivileged, minority or immigrant groups.

• Studies on intervention in the kindergarten/ higher education levels

• Studies on gender differences

• Studies on special education

• Studies on health inequalities in the student population

• Studies that do not describe the strategies or measures implemented.

• Studies that do not evaluate the efficacy of the intervention, whether by quantitative or qualitative methods.

The search yielded 195 results, and, in a first phase, the articles' abstracts and keywords were carefully analyzed. After this initial screening, 118 articles were excluded for not fitting the inclusion criteria (see Figure 1 ). Posterior closer and thorough reading reduced these studies to 20. Afterwards, we added a tailored search to our systematic revision, focusing on the Journal of Research of Educational Effectiveness. The keywords for this specific search were: educational achievement or academic achievement AND intervention AND minorities AND socioeconomic .

Figure 1 . PRISMA flow diagram.

Therefore, 13 articles from the Journal of Research on Educational Effectiveness were selected by screening the journal's titles and abstracts. The following analysis brought to light another 7 relevant articles portraying studies of measurement of interventions on school inequalities. These underwent the same procedure of analysis and classification as the previous articles in the EBSCO database.

The data retrieved from the Journal of Research on Educational Effectiveness were not obtainable from the EBSCO databases, and since this particular journal focuses on the evaluation of educational strategies, it was considered an additional value to the work, making the revision more complete and widespread.

The reason why some many articles did not fit the inclusion criteria stem from the fact that most of the studies retrieved showed an emphasis only around the causes and origins of achievement inequalities, such as the negative impact of belonging to a low SES or to a minority. Besides these factors, many articles highlighted gender differences in academic outcomes, which is not our focus here. In other cases, studies described other possible causal explanations for the differences in the students' performance, such as teachers' training and attitudes, school climate (without reference to any measure to transform it), violence in the school context, and resilience factors for at-risk or minority students. Moreover, some articles explored gifted students' programs, differential vocational choices for low SES, minorities, and gender groups, and tracking methodologies and, finally, many studies performed in the higher education system. Another exclusion criteria that surfaced often was not including any intervention.

Finally, the remaining articles were read thoroughly. As above mentioned, only 27 in total corresponded to the inclusion criteria at the end of this detailed reading.

The organization of the studies in a PICO framework (Population, Intervention, Comparators, Outcomes) allows for an overview of the diversity of measures and interventions collected ( Table 1 ).

Table 1 . PICO analysis.

The analysis was conducted according to several categories which were previously created, considering the studies' aims.

In the first place, we classified the articles into Strong, Weak or Mixed efficacy categories, according to the efficacy of the intervention, based on the impact described in the articles' results section ( Table 2 ). As it is of general knowledge, often the impact is not totally positive or negative. Often, the results are intricate and complex and need a thorough breakdown. In many cases, they indicate some measures of improvement in some respects, for some specific groups and show a decrease or maintenance in the outcomes in other groups or measures. Most studies analyzed in this revision of intervention strategies revealed such complexity.

Table 2 . Categories of efficacy level and object of intervention.

Another category we were initially interested in, was the type of intervention in terms of focus and scope. Since the objective is to increase performance in underachieving groups, many types and scopes of interventions have been put into practice in the last two decades. From the most specific, tailored action designed for difficulties in learning how to read, how to write, and how to develop a better understanding of mathematics or sciences, for example, to the more general national policies in education, there has been a wide range of practices. Subsequently, a more detailed analysis shows that the measures and strategies shown to have a more substantial impact, are very diverse and challenging to organize into categories. Our organizational choices were one in many possibilities, given the array of strategies. It was a categorization suggested by the material itself. The studies analyzed assembled in either a more pinpoint strategy, or a more holistic, integrated action, or even widespread national or state policy changes. Therefore, a definition of underlying themes was created as follows: Intervention focused in a learning basic ability/subject (mainly in the areas of reading, writing, and mathematics); Participation in state or community programs (after-school or summer compensation programs, state policies); Support to Families; Pedagogical interventions; Psychosocial Strategies; Whole school approaches ( Table 2 ).

To broaden the analysis and better organize these categories more concretely and perceptibly, we should look at some examples of the measures that have proven to be most effective ( Table 3 ).

Table 3 . Categories of educational intervention in the strong efficacy level.

6.1. Reading/writing/subject focused strategies

What emerged more frequently in this revision were approaches that focused on compensation strategies in reading/writing abilities, especially in the early years of schooling. These interventions are also frequently developed to address non-native-speaking learners' difficulties. Therefore, compensation techniques in Reading, Writing, and Comprehension comprise many of the studies reviewed as having a very good impact on learning skills. Here is a more descriptive presentation of some of the initiatives:

The Lindamood-Bell Learning Processes implemented a reading enhancement program in Colorado, in a minority urban district, with Title I Schools (with at least 40% Free Meal Students) to improve the reading scores, which were below average ( Sadoski and Willson, 2006 ). The Lindamood-Bell Method uses different senses to help students make connections between sounds, letters, and words. It also applies imagery to improve understanding of contents ( https://www.understood.org/articles/en/lindamood-bell-program-what-you-need-to-know ). There was an overall improvement in schools where the project was implemented compared to other schools in the control group, and it increased over the years. Variables like school size, minority students and FSM students' percentage variation were controlled to guarantee the effects are attributable only to the implementation.

In order to improve the reading comprehension outcomes of children in high-poverty schools, a large-scale comprehension intervention summer school was implemented, with lessons at the end of the school year. It stimulated home-based reading routines with narrative and informational books alongside regular assistance from the children's parents. The READS intervention (Reading Enhances Achievement During Summer) showed improved reading comprehension scores by 4% of a standard deviation ( Kim et al., 2016 ). Descriptively, the results suggest that the effect size in high-poverty schools (75–100% free lunch) was larger than the effect size in moderate-poverty schools.

A project designed to increase text-based analytical writing, called the Pathway Project, which uses a cognitive promotion approach, was also successful. It includes tools such as explicit instruction, “notational systems, think sheets, graphic organizers, prompts, planning strategies”, and learning communities ( Olson et al., 2017 , p. 3). The project target was secondary school students, specifically Latinos and mainstreamed English Learners from a large, urban, low-SES district, to develop academic writing skills. Analyses revealed significant effects on student writing outcomes in both years of the intervention. Moreover, Pathway students had higher odds than control students of passing the California High School Exit Exam in both years.

To evaluate the mnemonic effect of orthography on learning new vocabulary, Rosenthal and Ehri (2008) examined whether spelling could improve students' memory for pronunciations and meanings of new vocabulary words. Lower socioeconomic status minority 2nd and 5th graders were taught two sets of unfamiliar nouns and their meanings over several learning trials. Results show that orthographic knowledge benefited vocabulary learning and diminished dependence on phonological memory.

6.2. Whole school model

The “Building Assets, Reducing Risks” (BARR) program has presented a considerable positive impact in this category of interventions, as described by Borman et al. (2021) .

The BARR model is a comprehensive approach that uses eight interlinked strategies to build intentional staff and students' relationships. For instance, the core teams of teachers have scheduled time to meet and discuss students' strengths and challenges. “During these block/team meetings, teachers share with each other their individual experiences of students and collectively review real-time student data to identify interventions that may be helpful.” (p. 816). Seventy-five per cent of students were racial/ethnic minorities in this study. Results demonstrate that reductions in gaps in attainment and course passing for some subgroups were notorious (e.g., minority status and English Learners status, especially for the credits earned outcome). Also, English Language Arts scores significantly impacted minority students and those eligible for free or reduced-price lunches.

6.3. Pedagogical approaches

The International Baccalaureate (IB) program is an international initiative that promotes students' autonomy, learning and research abilities. The IB challenges learners to think critically and to learn in a flexible environment ( https://www.ibo.org/programmes/diploma-programme/ ). Mayer (2008) wanted to discover whether this highly demanding curriculum worked in urban, low-achieving schools. The main question placed here was if an efficiently structured academic program could increase the possibility for low-income students and students of color to achieve university admission.

Mayer used mixed methods to analyze the relationship between implementing a structured graduate studies preparatory program (IB) in a difficult school district, and academic achievement, in low-income Latino and African American students. The school where this intervention took place, Jefferson High School, is situated in an urban context, that serves mainly Latino, Black, and Southeast Asian students. Several learning scaffolds were included in this particular Title 1 school, such as counseling, academic enrichment courses, and social support. Examples of academic enrichment experiences were algebra or biology tutoring or community service. Social scaffolding included student retreats to develop leadership activities and prompt teacher-student positive relationships. Social support could also involve regular club attendance to improve motivation and establish mutual peer support.

Jefferson's students appeared to succeed, working with this demanding academic curriculum, alongside these scaffolding measures, motivational retreats, university preparation clubs, and tutoring. In 2006, 48 of the 55 diploma candidates achieved university admission. On the whole, the author concluded that the IB program effectively attracted and retained African American, Latino, and Native-American students, from lower SES levels.

Walsh (2011) studied two programs, explicitly designed to reduce racial disparity in university admission—Upward Bound and Talent Search—focusing on race/ethnicity and socioeconomic status, at a secondary level. “Upward Bound provides instruction in mathematics, laboratory sciences, foreign languages, composition, and literature as well as social and cultural capital” (p. 372). Participant students must, among other conditions, present a need for academic support if they are to attend graduate studies, be of a lower SES, and be the child of parents who have not completed postsecondary programs. Talent Search aims to help low-SES students in the graduate studies application process, giving them several measures of support through financial, career and academic counseling. Program participation significantly promotes low-SES African American and Hispanic students' graduate studies attendance.

Thompson et al. (2008) analyzed the effects of mathematics learning in a looping environment. In this school mechanism, students remain with one group of teachers for two or more years. This showed positive results on test scores for looping classes compared to their counterparts. Considering ethnicity, African American and Caucasian looping students achieved higher scores over the two-year looping period than did their non-looping peers.

6.4. Psychosocial interventions

An example of a psychosocial intervention targeted feelings of control over academic achievement in youngsters. Pizzolato et al. (2011) evaluated the effectiveness of a program based on Control Theory. More specifically, youngsters' perceptions of control over their academic achievement, while being able to envision themselves as successful adults, and correlate these expectations to current school behavior. The intervention focused on small group sessions, in a high school context, with students from a low-socioeconomic status community, with the goal of debating and planning strategies to enhance perceptions of control and purpose. The evidence showed a significant positive effect on GPA scores, through the coupled effect of internal control and a sense of enhancement.

The other two types of efficacy level interventions are listed in Table 4 .

Table 4 . Mixed or weak efficacy categories.

7. Discussion

This literature review aimed at collecting and analyzing current educational strategies to oppose achievement inequalities in minority and/or lower socioeconomic status students. Our approach was systematic and broad, in order to include an array of studies that could demonstrate current trends and models set in elementary and secondary schools. It was our goal, from the beginning, to reach and integrate as many interventions and measures as possible, hence our choice for a systematic revision instead of a meta-analysis methodology. The qualitative aspects of studies were equally included, considering the perspective of participants as a reliable source about the phenomenon and its effects. This option maintains its consistency, even though the data obtained, and the treatments used differ. This decision was based on the premise that different (qualitative or quantitative) methodological choices, and their results are equally reliable.

Our findings exposed a diversity of interventions, from the most topic-specific, focusing on fundamental learning competencies, such as reading, writing, and comprehension, to the broadest organizational and political changes in the educational system.

The objective was to compile all studies that evaluated these strategies, allowing for evidence-based analysis. Many of these showed mixed results, that is, the efficacy was proven in some of the treatment groups, but not all. Nevertheless, some success was achieved in reducing these gaps. In these mixed-results interventions, we can consider that each subgroup's cultural or historical idiosyncrasies function and result in different outcomes. One approach may have a strong impact on Hispanic students, for instance, and not on Black students, while other methods may be more effective with other minorities. Overall, these findings emphasize the need for a situated and contextual perspective to evidence-based approaches. Interventions that are tailored to local and group characteristics show more efficacy. Strategies intended to be universal and replicable, without careful adaptation to, knowledge of or active involvement from the target population, may fail for lack of adequacy to existing needs, particular problems and constraints, but also lack of recognition of existing resources and facilitators.

Looking at those actions that achieved stronger transformations, altogether, it was also notorious that the most specific interventions designed to address a particular impairment, usually an essential cornerstone for learning, like reading skills, and early mathematical comprehension, have been proven more effective. In terms of research, especially of quantitative nature, having fewer independent and dependent variables may increase the odds of showing clearer results. The broader the scope of intervention, the more complex and entangled the results become. As such, there is a clear need for more robust methodological designs, including mixed-methods and longitudinal approaches, in more holistic interventions.

Nevertheless, some family- and community-based initiatives have been proven effective, such as summer programs with active parental involvement, whole-school approaches or projects targeting group discrimination prevention.

There were fewer interventions, in our revision, that presented very low or null positive impacts. These presented a very varied nature, but it's noteworthy that several attempts to achieve better results for disadvantaged population via vouchers or lottery selection, to access private schooling, for example, have not been fruitful. We may discuss this outcome from many perspectives, but, perhaps, looking at the discrepancy of curricula demand, coming from low achieving public schools and entering high achieving private schools, where most population have a normative, strong middle class cultural and social background, may constitute an obstacle for achievement. This contrast may arise for academic reasons (lack of previous knowledge, different pedagogies) or for psychological and social limitations, such as poor social integration, competitive peer environment, high pressure for excellent results.

8. Limitations and future research possibilities

An attempt to gather, organize and make coherent analysis of such a vast range of educational strategies and measures is full of obstacles. How to group different studies, with some consistency, how to create a categorization that would reveal some worthy results, is a challenging decision. A striking finding is the predominance of the United States in this area, with poor representation from other countries, especially in the African and South American regions. Even in Europe, where this revision took place, the amount of investigation measuring, whether by qualitative, quantitative, ethnographic, or other methods, educational strategies' impact, is scarce.

On the whole, there is a clear need for more systematic knowledge in this field. The evaluation of strategies, projects, and measures in education, designed to improve performance and reduce drop-out, in these historically excluded, disadvantaged social groups, is still scarce. Without rigorous and continuous research, decision-making in this area could fail: How to improve what already is being implemented and how to start something else anew, without guidelines and references from other empirically proven strategies? The need for regular, well-tailored, consistent research is huge, for we are aware that many schools, institutions, education cabinets, and many other organisms have been creating and implementing educational projects, without any mechanism to monitor and control their outputs. To learn more about educational intervention in this area, we must do better in terms of program evaluation.

9. Conclusion

These findings emphasize the need to envision the ecological nature of social transformations, where all pertaining elements should be considered in the process of constructing a more equalitarian society. Schools cannot entail this mission alone, for it derives from deep seeded roots of oppression and discrimination, which demands time, a collective effort and will to confront.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

CC-G, TN, and IM contributed to conception and design of the study. CC-G did the systematic search, retrieval of articles, analyzed and organized the data, and performed the content analysis. TN and IM reviewed every step of the process. All authors contributed to manuscript revision, read, and approved the submitted version.

This work was supported by FCT—Foundation for Science and Technology, IP, and co-funded by the European Social Fund, under the Human Capital Operational Programme (POCH) from Portugal 2020 Programme, within the Doctoral Programme in Education of the University of Porto (grant no. 2020.5877.BD). And by FCT within the scope of the strategic program of CIIE—Center for Research and Intervention in Education at the University of Porto (refs. UID/CED/00167/2019 and UIDB/00167/2020).

Acknowledgments

We would like to thank Filipa Cesar for her suggestions and aid in the structuring.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Ashraf, B., Singh, A., Uwimpuhwe, G., Higgins, S., and Kasim, A. (2021). Individual participant data meta-analysis of the impact of educational interventions on pupils eligible for free school meals. Br. Educ. Res. J. 47, 1675–1699. doi: 10.1002/berj.3749

CrossRef Full Text | Google Scholar

August, D., Branum-Martin, L., Cárdenas-Hagan, E., Francis, D. J., Powell, J., Moore, S., et al. (2014). Helping ELLs meet the common core state standards for literacy in science: the impact of an instructional intervention focused on academic language. J. Res. Educ. Eff. 7, 54–82. doi: 10.1080/19345747.2013.836763

Azzolini, D., Schnell, P., and Palmer, J. (2012). Educational achievement gaps between immigrant and native students in two “new immigration countries”: Italy and Spain in comparison. Ann. Am. Acad. Polit. Soc. 643, 46–77. doi: 10.1177/0002716212441590

PubMed Abstract | CrossRef Full Text | Google Scholar

Bécares, L., and Priest, N. (2015). Understanding the influence of race/ethnicity, gender, and class on inequalities in academic and non-academic outcomes among Eighth-Grade Students: Findings from an intersectionality approach. PLoS ONE 10, 1–17. doi: 10.1371/journal.pone.0141363

Berkowitz, R., Moore, H., Astor, R. A., and Benbenishty, R. (2017). A research synthesis of the associations between socioeconomic background, inequality, school climate, and academic achievement. Rev. Educ. Res. 87, 425–469. doi: 10.3102/0034654316669821

Bitler, M., Domina, T., Penner, E., and Hoynes, H. (2015). Distributional analysis in educational evaluation: a case study from the New York City voucher program. J. Res. Educ. Eff. 8, 419–450. doi: 10.1080/19345747.2014.921259

Borman, T. H., Bos, H., Park, S. J., and Auchstetter, A. (2021). Impacting 9th grade educational outcomes: results from a multisite randomized controlled trial of the BARR model. J. Res. Educ. Eff. 14:4, 812–834. doi: 10.1080/19345747.2021.1917027

Burger, K. (2019). The socio-spatial dimension of educational inequality: a comparative European analysis. Stud. Edu. Evaluat. 62, 171–186. doi: 10.1016/j.stueduc.2019.03.009

Coimbra, S., and Fontaine, A. (2015). “Resiliência e habilidades sociais: reflexões conceituais e práticas para uma nova geração,” in Zilda A. P. Del Prette, Adriana Benevides Soares, Camila de Sousa Pereira-Guizzo, Marcia Fortes Wagner, and Vanessa Barbosa Romera Leme. Habilidades sociais: diálogos e intercâmbios sobre a pesquisa e prática . (Novo Hamburgo: Sinopsys), p. 186–220.

Google Scholar

Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D., et al. (1966). Equality of Educational Opportunity . Washington: U.S. Department of Health, Education, and Welfare, Office of Education.

Colgren, C., and Sappington, N. (2015). Closing the achievement gap means transformation. Edu. Leadership Rev. Doctoral Res. 2, 24–33.

Dee, T. S. (2015). Social identity and achievement gaps: evidence from an affirmation intervention. J. Res. Edu. Effect. 8, 149–168. doi: 10.1080/19345747.2014.906009

Dietrichson, J., Bøg, M., Filges, T., and Klint Jørgensen, A. M. (2017). Academic interventions for elementary and middle school students with low socioeconomic status: a systematic review and meta-analysis. Rev. Educ. Res. 87, 243–282. doi: 10.3102/0034654316687036

Driessen, G., and Dekkers, H. (2008). Dutch policies on socio-economic and ethnic inequality in education. Int. Soc. Sci. J. 59, 449–464. doi: 10.1111/j.1468-2451.2009.01678.x

Duncan, G. J., Morris, P. A., and Rodrigues, C. (2011). Does money really matter? estimating impacts of family income on young children's achievement with data from random-assignment experiments. Dev. Psychol , 47, 1263–1279. doi: 10.1037/a0023875

Easterbrook, M. J., and Hadden, I. R. (2021). Tackling educational inequalities with social psychology: identities, contexts, and interventions. Soc. Issues Policy Rev. 15, 180–236. doi: 10.1111/sipr.12070

Elias, M. J., White, G., and Stepney, C. (2013). Surmounting the challenges of improving academic performance: closing the achievement gap through social-emotional and character development. J. Urban Learn. Teach. Res. 10, 14–24.

Erickson, H. H., Mills, J. N., and Wolf, P. J. (2021). The effects of the louisiana scholarship program on student achievement and college entrance. J. Res. Educ. Eff. 14, 861–899. doi: 10.1080/19345747.2021.1938311

Ferraz, H., Neves, T., and Nata, G. (2018). A eficácia dos programas de educação compensatória nos resultados escolares: análise do programa nacional português de educação compensatória ao longo de 13 anos. Ensaio: aval. pol. públ. educ. 26, 100. doi: 10.1590/s0104-40362018002601036

Furgione, B., Evans, K., Russell, W. B., and Jahani, S. (2018). Divided we test: Proficiency rate disparity based on the race, gender, and socioeconomic status of students on the Florida US history end-of-course assessment. J. Soc. Stud. Educ. Res. 9, 62–96.

García, E., and Weiss, E. (2017). Education inequalities at the school starting gate. Econ. Policy Institute 1–101.

Gillborn, D., Demack, S., Rollock, N., and Warmington, P. (2017). Moving the goalposts: Education policy and 25 years of the Black/White achievement gap. Br. Educ. Res. J. 43, 848–874. doi: 10.1002/berj.3297

Gonçalves, F. d. O., and França, M. T. A. (2008). Transmissão intergeracional de desigualdade e qualidade educacional: avaliando o sistema educacional brasileiro a partir do SAEB 2003. Ensaio: aval. pol. públ. educ. , 16, 639–662. doi: 10.1590/S0104-40362008000400009

Grooms, A. A., and Williams, S. M. (2015). The reversed role of magnets in st. Louis: implications for black student outcomes. Urban Edu. 50, 454–473. doi: 10.1177/0042085913516131

Harris, J. C. (2019). Changing context: do magnet schools improve student achievement in a modern setting? J. School Choice 13, 305–334. doi: 10.1080/15582159.2019.1594605

Hung, M., Smith, W. A., Voss, M. W., Franklin, J. D., Gu, Y., Bounsanga, J., et al. (2020). Exploring student achievement gaps in school districts across the United States. Educ. Urban Soc. 52, 175–193. doi: 10.1177/0013124519833442

Kao, G., and Thompson, J. S. (2003). Racial and ethnic stratification in educational achievement and attainment. Annu. Rev. Sociol. 29, 417–442. doi: 10.1146/annurev.soc.29.010202.100019

Kim, J. S., Guryan, J., White, T. G., Quinn, D. M., Capotosto, L., and Kingston, H. C. (2016). Delayed effects of a low-cost and large-scale summer reading intervention on elementary school children's reading comprehension. J. Res. Educ. Eff. 9, 1–22. doi: 10.1080/19345747.2016.1164780

Lee, V. E., and Burkam, D. T. (2002). Inequality at the starting gate: Social background differences in achievement as children begin school . Economic Policy Institute, Washington, DC, United States.

Lee, V. E., and Ready, D. D. (2009). High school curriculum: three phases of contemporary research and reform. Future Child. 19, 135–136. doi: 10.1353/foc.0.0028

Martins, S., Mauritti, R., Nunes, N., Romão, A. L., and da Costa, A. F. (2016). A educação ainda é importante para a mobilidade social? Uma perspetiva das desigualdades educacionais da Europa do Sul no contexto europeu. Revista Portuguesa de Educação 29, 261. doi: 10.21814/rpe.7920

Mayer, A. P. (2008). Expanding opportunities for high academic achievement. J. Adv. Acad. 19, 202–235. doi: 10.4219/jaa-2008-772

Mckown, C. (2013). Social equity theory and racial-ethnic achievement gaps. Child Dev. 84, 1120–1136. doi: 10.1111/cdev.12033

Merolla, D. M., and Jackson, O. (2019). Structural racism as the fundamental cause of the academic achievement gap. Sociology Compass 13, 1–14. doi: 10.1111/soc4.12696

CrossRef Full Text

Michelmore, K., and Dynarski, S. (2017). The gap within the gap. AERA Open 3, 233285841769295. doi: 10.1177/2332858417692958

Mishel, L., Josh, B, Elise, G, and Heidi, S. (2012). The State of Working America, 12th Edn . Ithaca, NY: Economic Policy Institute; ILR Press: Cornell University.

Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberatî, A., Petticrew, M., et al. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev. 4, 1–9.

PubMed Abstract | Google Scholar

Nomi, T. (2009). The effects of within-class ability grouping on academic achievement in early elementary years. J. Res. Educ. Eff. 3, 56–92. doi: 10.1080/19345740903277601

OECD (2020). Education at a Glance 2021: OECD Indicators . Paris: OECD. Available online at: https://www.oecd-ilibrary.org/sites/db0e552c-en/index.html?itemId=/content/component/db0e552c-en

Olson, C. B., Matuchniak, T., Chung, H. Q., Stumpf, R., and Farkas, G. (2017). Reducing achievement gaps in academic writing for latinos and english learners in grades. J Edu Psychol . 109, 1–21. doi: 10.1037/edu0000095

Pizzolato, J. E., Brown, E. L., and Kanny, M. A. (2011). Purpose plus: supporting youth purpose, control, and academic achievement. New Directions Youth Develop. 132, 75. doi: 10.1002/yd.429

Reynolds, A. J., Magnuson, K. A., and Ou, S. R. (2010). Preschool-to-third grade programs and practices: a review of research. Child. Youth Serv. Rev. 32, 1121–1131. doi: 10.1016/j.childyouth.2009.10.017

Rijkschroeff, R., ten Dam, G., Duyvendak, J. W., de Gruijter, M., and Pels, T. (2005). Educational policies on migrants and minorities in the Netherlands: Success or failure? J. Edu. Policy 20, 417–435. doi: 10.1080/02680930500132148

Rosenthal, J., and Ehri, L. C. (2008). The mnemonic value of orthography for vocabulary learning. J. Educ. Psychol. 100, 175–191. doi: 10.1037/0022-0663.100.1.175

Rosenthal, J., and Ehri, L. C. (2011). Pronouncing new words aloud during the silent reading of text enhances fifth graders' memory for vocabulary words and their spellings. Read. Writ. Interdisciplinary J. 24, 921–950. doi: 10.1007/s11145-010-9239-x

Sadoski, M., and Willson, V. L. (2006). Effects of a theoretically based large-scale reading intervention in a multicultural urban school district. Am. Educ. Res. J. 43, 137–154. doi: 10.3102/00028312043001137

Sirin, S. R. (2005). Socioeconomic status and academic achievement: a meta-analytic review of research. Rev. Educ. Res. 75, 417–453. doi: 10.3102/00346543075003417

Snellman, K., Silva, J. M., Frederick, C. B., and Putnam, R. D. (2015). The engagement gap: social mobility and extracurricular participation among American youth. Ann. Am. Acad Polit. Soc. Sci. 657, 194–207. doi: 10.1177/0002716214548398

Stevens, P. A. J. (2007). Researching race/ethnicity and educational inequality in English secondary schools: a critical review of the research literature between 1980 and 2005. Rev. Educ. Res. 77, 147–185. doi: 10.3102/003465430301671

Stiglitz, J. (2014). The Price of Inequality: How Today's Divided Society Endangers our Future . Pontifical Academy of Social Sciences. Acta 19: Vatican City.

Sung, Y. T., Tseng, F-. L., Kuo, N-. P., Chang, T-. Y., and Chiou, J.-M. (2014). Evaluating the effects of programs for reducing achievement gaps: a case study in Taiwan. Asia Pacific Edu. Rev. 15, 99–113. doi: 10.1007/s12564-013-9304-7

Thompson, N. L., Fuller, B., Hare, R. D., and Miller, N. C. (2008). Evaluating mathematics achievement of middle school students in a looping environment. Sch. Sci. Math. 110, 298–308. doi: 10.1111/j.1949-8594.2010.00038.x

VanDerHeyden, A. M., and Codding, R. S. (2015). Practical effects of classwide mathematics intervention. School Psych. Rev. 44, 169–190. doi: 10.17105/spr-13-0087.1

Walsh, R. (2011). Helping or hurting : are adolescent intervention programs minimizing racial inequality? Educ. Urban Soc. 43, 370–395. doi: 10.1177/0013124510380419

What Works Clearinghouse (2019). Passport Reading Journeys. Institute of Education Science . Available online at: https://eric.ed.gov/?q=teacher+education+mathandft=onandpg=96andid=ED600567

White, K. R. (1982). The relation between socioeconomic status and academic achievement. Psychol. Bull . 3, 461–481. doi: 10.1037/0033-2909.91.3.461

Zhou, Y. (2015). Educational choice and marketization in Hong Kong: the case of direct subsidy scheme schools. Asia Pacific Edu. Rev. 16, 627–636. doi: 10.1007/s12564-015-9402-9

Keywords: systematic review, educational gap, intervention, socioeconomic, minorities, immigrants

Citation: Cabral-Gouveia C, Menezes I and Neves T (2023) Educational strategies to reduce the achievement gap: a systematic review. Front. Educ. 8:1155741. doi: 10.3389/feduc.2023.1155741

Received: 31 January 2023; Accepted: 10 April 2023; Published: 26 May 2023.

Reviewed by:

Copyright © 2023 Cabral-Gouveia, Menezes and Neves. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Tiago Neves, neves.tiago@yahoo.com

This article is part of the Research Topic

Persistent Inequality in Urban Educational Organizations – Current Issues and Possible Solutions

Racial and Ethnic Achievement Gaps

Racial and ethnic inequality in education has a long and persistent history in the United States. Beginning in 1954, however, when the Supreme Court ruled in Brown v. Board of Education that racial segregation of public schools was unconstitutional, some progress has been made in improving racial educational disparities. But that progress has been slow, uneven, and incomplete.

One key set of measures of racial educational equality are racial achievement gaps—differences in the average standardized test scores of white and black or white and Hispanic students. Achievement gaps are one way of monitoring the equality of educational outcomes.

The series of figures below describe recent trends and patterns in racial achievement gaps.

Page contents

Over the past 40 years, white-black and white-Hispanic achievement gaps have been declining, albeit unsteadily.

Every few years, a sample of 9-, 13-, and 17-year-olds from around the United States are given tests in math and reading as part of the National Assessment of Educational Progress (NAEP). NAEP, sometimes called "The Nation’s Report Card," is designed to provide the public and policymakers with an objective assessment of the math and reading skills of American children. Because NAEP has used the same tests since the 1970s, we can use it to compare the reading and math skills of children today with those of their parents’ generation. We can also use NAEP to examine trends in the white-black and white-Hispanic achievement gaps. These trends are illustrated in the figure below.

White-black and white-Hispanic achievement gaps have, in general, narrowed substantially since the 1970s in all grades and in both math and reading. The gaps narrowed sharply in the 1970s and the first half of the 1980s, but then progress stalled. In fact, some of the achievement gaps grew larger in the late 1980s and the 1990s. Since the 1990s, however, achievement gaps in every grade and subject have been declining. As of 2012, the white-black and white-Hispanic achievement gaps were 30-40% smaller than they were in the 1970s. Nonetheless, the gaps are still very large, ranging from 0.5 to 0.9 standard deviations.

How to read this figure

Each line in the figure shows the trend in the achievement gap in math or reading for a specific pair of racial/ethnic groups (white-black or white-Hispanic) at a particular age (9-, 13-, or 17-years-old). The achievement gaps are measured in standard deviation units (for more information on how the gaps are computed, see here ). The trend lines are smoothed from the gaps estimated in various years. Holding the mouse over a line will reveal the underlying data from which the smooth curve was estimated. The bars around each annual estimate indicate the 95% confidence intervals for each year’s estimated achievement gap. Although the achievement gap in any one year is estimated with some uncertainty, the general pattern evident in the trends is clear.

Achievement gaps have been narrowing because Black and Hispanic students’ scores have been rising faster than those of White students.

Achievement gaps have been closing because Black and Hispanic students’ scores have improved very rapidly over the last 30 years. Indeed, among Black and Hispanic students, the average 9-year-old student today scores almost as well on the NAEP math tests as the average 13-year-old did in 1978; the average 13-year old today scores almost as well as the average 17-year-old in 1978. In other words, black and Hispanic students today are roughly three years ahead of their parents’ generation in math skills. In reading, they are roughly two to three years ahead of their parents. White students’ scores have also improved, but not by as much. These trends are illustrated in the figure below.

For each subject and age group, the figure displays three lines, each of which shows the trend in the average NAEP scores for white, black, or Hispanic students. The vertical axis shows NAEP scores. To help interpret these scores, the horizontal lines indicate the type of skills that students must demonstrate to score at various levels (more detailed information on the NAEP performance levels is available here ). Each line’s label (on the right) indicates the overall change in average scores for that group since the first NAEP test shown (in the 1970s).

Achievement gaps in some states are larger than in others.

The white-black and white-Hispanic achievement gaps vary considerably among states. This is evident in the figures below, which show state-level achievement gaps for the years 1990-2013. These gaps are estimated from a version of the NAEP tests (called Main NAEP ") that has been given to samples of students in each state every two years since 2003 and in some states from as early as 1990.

In some states, particularly those in the upper Midwest, like Wisconsin, Michigan, Illinois, and Minnesota, the white-black achievement gap has generally been larger than a standard deviation over the last decade, regardless of grade or subject. Some other states, like Connecticut and Nebraska, also have white-black gaps this large, as does the District of Columbia, where the gap is well over 1.5 standard deviations. In states with small black populations, like West Virginia, Hawaii, Idaho, Wyoming, Montana, Vermont, and New Hampshire, for example, the gaps are consistently smaller, typically only half as large as in the states with the largest gaps.

The same is true of the white-Hispanic achievement gaps. In some states, most notably the New England states of Connecticut, Massachusetts, and Rhode Island, but also in California, Colorado, Minnesota, and in the District of Columbia, the white-Hispanic gap is quite large, on the order of 0.90 to 1.00 standard deviations (or 1.5 standard deviations in the District of Columbia). In West Virginia and Vermont, however, the gaps only 0.30 or smaller, only one-third the size as in the states with the largest gaps.

In some cases, the gaps are large because white students in these states score particularly high on the NAEP tests; in other cases, the gaps are large because black or Hispanic students score poorly. For example, the large white-Hispanic gap in California is largely due to the low average scores of California Hispanic students (who have among the lowest average scores in the country in math or reading), not the high performance of white students (who perform at roughly the average among white students nationally). Conversely, the large white-black gap in Minnesota is not due to black Minnesota students’ particularly low scores (they are near or slightly below the national average), but is due to the fact that white students in Minnesota have very high scores.

Select a gap (white-black or white-Hispanic), a grade (4th or 8th), and a test subject (math or reading) from the pull-down menus. The slider can be used to select a year, or to scroll through years from 1990 to 2013, though gap estimates are not available for all states in all years, particularly from 1990-2002. To highlight a particular state in the figure, select it from the "Select state" pull-down menu.

The map shows the size of the achievement gap in each state, for the selected group, grade, subject, and year, measured in standard deviation units . NAEP is not administered in every year for every subject and grade; states without data for a particular year, subject, group, and grade combination are shaded in grey. In addition to the map, the figure to the left shows the average test scores for each group in each state. These average scores are measured on the Main NAEP score scale (which is slightly different than the NAEP scale in the figure above; information on the Main NAEP scales is here ). On this figure, the states can be sorted, using the "Sort by" pull-down menu, to examine the relative performance of each state’s white, black, and Hispanic students.

In the last two decades, racial achievement gaps have been narrowing in most states, but have widened in a small number of states.

Although the white-black and white-Hispanic achievement gaps have been narrowing nationally over the last decade or more, the rate at which these gaps are changing varies among the states. The Main NAEP data provide repeated measures of the size of these achievement gaps in each of the states between 1990 and 2013.

On average, the within-state white-black achievement gaps have been narrowing at a rate of roughly 0.05 standard deviations per decade since 2003. The corresponding rate for white-Hispanic gaps is roughly 0.10 standard deviations per decade. Although the gaps are, on average, closing, they are doing so very slowly, compared to their current size.

Nonetheless, there are some states where the gaps are closing much more rapidly. In seven states (District of Columbia, New York, West Virginia, Louisiana, Arkansas, New Jersey, and Michigan) the white-black gaps are closing at a rate of at least 0.20 standard deviations per decade, four times the rate in the average state. The white-Hispanic gap has narrowed at this rate in 10 states, many of them Southern and Midwestern states with small but growing Hispanic populations (District of Columbia, Delaware, Georgia, Tennessee, Nebraska, Iowa, Illinois, Indiana, Arizona, and Michigan).

Likewise, many states that have seen no significant change from 2003-2013 in the white-black achievement gap (21 states) or the white-Hispanic gap (28 states). In only three states (Maine, Vermont, and Colorado) has the white-black gap increased significantly in the last decade. In only one has the white-Hispanic gap increased (West Virginia).

The figure below illustrates the trend in each state’s achievement gaps from 1990-2013.

Select a gap (white-black or white-Hispanic), a grade (4th or 8th), and a test subject (math or reading) from the pull-down menus. Each line in the figure shows the trend in this achievement gap for a given state. The achievement gaps are measured in standard deviation units (for more information on how the gaps are computed and what a standard deviation is, see here ). The trend lines are smoothed from the gaps estimated in various years. Holding the mouse over a line will reveal the underlying data from which the smooth curve was estimated. The bars around each annual estimate indicate the 95% confidence intervals for each year’s estimated achievement gap. Although the achievement gap in any one year is estimated with some uncertainty, the general pattern evident in the trends is clear.

State racial achievement gaps are strongly correlated with state racial socioeconomic disparities.

One potential explanation for racial achievement gaps is that they are largely due to socioeconomic disparities between white, black, and Hispanic families. Black and Hispanic children’s parents typically have lower incomes and lower levels of educational attainment than white children’s parents. Because higher-income and more-educated families typically can provide more educational opportunities for their children, family socioeconomic resources are strongly related to educational outcomes. If racial socioeconomic disparities are the primary explanation for racial achievement gaps, we would expect achievement gaps to be largest in places where racial socioeconomic disparities are largest, and we would expect them to be zero in places where there is no racial socioeconomic inequality.

The figure below suggest this explanation is at least partly true. Achievement gaps are strongly correlated with racial gaps in income, poverty rates, unemployment rates, and educational attainment. When these four factors are combined into a single index of racial socioeconomic disparities, the correlation between state achievement gaps and state racial socioeconomic disparities is high: for white-black gaps the correlation is 0.61-0.68; for white-Hispanic gaps it is 0.83-0.86. A large part of the variation among states’ racial achievement gaps is attributable to variation in states’ racial socioeconomic disparities.

Nonetheless, even in states where the racial socioeconomic disparities are near zero (typically states with small black or Hispanic populations), achievement gaps are still present. This suggests that socioeconomic disparities are not the sole cause of racial achievement gaps.

Select a gap (white-black or white-Hispanic), a grade (4th or 8th), and a test subject (math or reading) from the pull-down menus. Select a socioeconomic factor from the "Socioeconomic Disparity Measure " pull-down menu. The figure plots the chosen achievement gap (on the vertical axis) and socioeconomic disparity measure (on the horizontal axis) for each state. The line in each figure illustrates the average association between the two variables plotted : a line that slopes upward to the right indicates that states with larger white-minority socioeconomic disparities tend to have larger white-minority achievement gaps.

The construction of each of the socioeconomic measures available is described here . The "socioeconomic disparities index" is a composite of the other four measures (the racial income gap, education gap, poverty ratio, and unemployment ratio). Each of the measures is scaled so that the vertical axis is placed at the value corresponding to racial equality (i.e., a white-black income gap of 0 means that whites and blacks have equal incomes, on average; a white-black poverty ratio of 1 means that white and black families have equal poverty rates).

Some states’ achievement gaps are higher or lower than would be predicted by their socioeconomic conditions alone.

Despite the fact a state’s racial socioeconomic disparity is a very good predictor of its racial achievement gap (as is clear in the figure above), some states with similar levels of socioeconomic disparities have substantially different achievement gaps. For example, New Jersey and Wisconsin have very similar (and very high) levels of white-black socioeconomic disparities, but the white-black math achievement gap in Wisconsin is considerably larger (roughly 0.25 standard deviations larger) than in New Jersey. This suggests that socioeconomic disparities are not the sole cause of racial achievement gaps. Other factors—including potentially the availability and quality of early childhood education, the quality of public schools, patterns of residential and school segregation, and state educational and social policies—may play important roles in reducing or exacerbating racial achievement gaps.

Select a gap (white-black or white-Hispanic), a grade (4th or 8th), and a test subject (math or reading) from the pull-down menus. The map and bar chart show how much larger or smaller the achievement gap in each state is than we would predict based on its racial socioeconomic disparities alone. A value of 0.20, for example, indicates that a state’s achievement gap is 0.20 standard deviations larger than what we would predict based on the association between socioeconomic disparities and achievement gaps shown in the figure above. More detail on how these values were computed is here .

Design supported by Bootstrap and Glyphicons . Created by Naderstat .

Share full article

Supported by

Education Gap Grows Between Rich and Poor, Studies Say

By Sabrina Tavernise

Feb. 9, 2012

WASHINGTON — Education was historically considered a great equalizer in American society, capable of lifting less advantaged children and improving their chances for success as adults. But a body of recently published scholarship suggests that the achievement gap between rich and poor children is widening, a development that threatens to dilute education’s leveling effects.

It is a well-known fact that children from affluent families tend to do better in school. Yet the income divide has received far less attention from policy makers and government officials than gaps in student accomplishment by race.

Now, in analyses of long-term data published in recent months, researchers are finding that while the achievement gap between white and black students has narrowed significantly over the past few decades, the gap between rich and poor students has grown substantially during the same period.

“We have moved from a society in the 1950s and 1960s, in which race was more consequential than family income, to one today in which family income appears more determinative of educational success than race,” said Sean F. Reardon, a Stanford University sociologist. Professor Reardon is the author of a study that found that the gap in standardized test scores between affluent and low-income students had grown by about 40 percent since the 1960s, and is now double the testing gap between blacks and whites.

In another study, by researchers from the University of Michigan , the imbalance between rich and poor children in college completion — the single most important predictor of success in the work force — has grown by about 50 percent since the late 1980s.

The changes are tectonic, a result of social and economic processes unfolding over many decades. The data from most of these studies end in 2007 and 2008, before the recession’s full impact was felt. Researchers said that based on experiences during past recessions, the recent downturn was likely to have aggravated the trend.

We are having trouble retrieving the article content.

Please enable JavaScript in your browser settings.

Thank you for your patience while we verify access. If you are in Reader mode please exit and log into your Times account, or subscribe for all of The Times.

Thank you for your patience while we verify access.

Already a subscriber? Log in .

Want all of The Times? Subscribe .

Featured Topics

Featured series.

A series of random questions answered by Harvard experts.

Explore the Gazette

Read the latest.

Examining the duality of Israel

$Boy looking at tutor doing math on a chalkboard via a computer.$

One way to help big groups of students? Volunteer tutors.

Footnote leads to exploration of start of for-profit prisons in N.Y.

Student achievement gap same after nearly 50 years, study says, but at least it’s not getting wider, say authors, who cite decline in teacher quality as offsetting programs like head start.

The achievement gap between disadvantaged and well-off students is as wide today as it was for children born in 1954 when it comes to tests in math, reading, and science, researchers report in a new article for the journal Education Next.

However, the study contradicts research suggesting that socioeconomic achievement gaps have substantially widened in recent years.

“After looking at a comprehensive, systematic set of student assessments, we are unable to confirm earlier, more limited research that purports to show income-achievement differences have grown dramatically,” said the journal’s senior editor, Paul E. Peterson, a professor of government at Harvard, director of the University’s Program on Education Policy and Governance, and a senior fellow at the Hoover Institution.

The authors used a representative sample of student performance data on four national assessments — designed to be comparable over time — administered to students born between 1954 and 2001: both the long-term trend and main versions of the National Assessment of Educational Progress (NAEP); the Trends in International Mathematics and Science Survey (TIMSS); and the Program for International Student Assessment (PISA). The sample includes a total of 98 tests administered over 47 years to more than 2.7 million students at around ages 14 and 17.

More like this

The costs of inequality: Education’s the one key that rules them all

Rapid evolution, illustrated

Among the key findings:

Extremely disadvantaged students are three to four years behind their more affluent peers. The current gap between the highest and lowest 10 percent of the socioeconomic distribution is roughly three to four years of learning , or more than one standard deviation . The gap between students from the highest and lowest 25 percent of the socio-economic distribution amounts to more than two-and-a-half years of learning.

The opportunity gap has not wavered over the last half-century. For students born in 2001, the gap between the highest and lowest 10 percent of the socioeconomic distribution is only 10 percentage points lower than it was for those born in 1954. This gap between those in the top and bottom 25 percent opened very slightly during the two decades after the 1954 cohort, only to settle back to barely below 80 percent for the cohort born in 2001.

Gaps between other student subgroups also remain nearly constant. The authors find a persistent achievement gap between students eligible for free and reduced-price lunch compared with those who are not eligible. And race remains a factor: While the black-white achievement gap did narrow in the early decades of the period under study, it has plateaued for the past quarter-century.

Overall performance improves among 14-year-old students over time, but these gains fade by age 17. Performance in math, reading, and science by 14-year-old students has improved steadily on average throughout the past five decades, at approximately 8 percent per decade. However, gains among 17-year-old students amount to only about 2 percent per decade, and none at all for the last quarter century.

The authors — who also include Laura M. Talpey, a research associate at Stanford, and Ludger Wössmann, a professor of economics at the University of Munich — suggest that two offsetting educational developments may have contributed to the unwavering achievement gap.

“On the positive side, the country has launched multiple compensatory education programs, including Head Start, school desegregation, federal aid to districts with low-income students, special-education programs, and court-ordered reductions in fiscal inequalities across school districts,” said Eric A. Hanushek, the Paul and Jean Hanna Senior Fellow at the Hoover Institution of Stanford University. “On the negative side, we appear to be have seen a decline in teacher quality that has had particularly dire consequences for low-income students.”

Share this article

Bringing back a long extinct bird

Scientists sequence complete genome of bush moa, offering insights into its natural history, possible clues to evolution of flightless birds

Women who follow Mediterranean diet live longer

Large study shows benefits against cancer, cardiovascular mortality, also identifies likely biological drivers of better health

Harvard-led study IDs statin that may block pathway to some cancers

Cholesterol-lowering drug suppresses chronic inflammation that creates dangerous cascade

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
Pediatric Health Med Ther

Black-White Achievement Gap: Role of Race, School Urbanity, and Parental Education

Shervin assari.

1 Departments of Family Medicine, Charles R Drew University of Medicine and Science, Los Angeles, CA, 90059, USA

Abbas Mardani

2 Nursing Care Research Center, School of Nursing and Midwifery, Iran University of Medical Sciences, Tehran, Iran

Maryam Maleki

3 School of Nursing and Midwifery, Shahroud University of Medical Sciences, Shahroud, Iran

Shanika Boyce

4 Departments of Pediatrics, Charles R Drew University of Medicine and Science, Los Angeles, CA, 90059, USA

Mohsen Bazargan

5 Departments of Family Medicine, University of California, Los Angeles (UCLA), Los Angeles, CA, 90095, USA

Recent research on Marginalization-related Diminished Returns (MDRs) has documented weaker boosting effects of parental educational attainment on educational outcomes of Black than White students. Such MDRs of parental education seem to contribute to the Black-White achievement gap. Given that Blacks are more likely than Whites to attend urban schools, there is a need to study if these MDRs can be replicated at both urban and suburban schools.

To test the contribution of diminished returns of parental educational attainment on the Black-White achievement gap in urban and suburban American high schools.

A cross-sectional study that used baseline Education Longitudinal Study (ELS-2002) data, a nationally representative study of 10th grade adolescents in the United States. This study analyzed 8315 youths who were either non-Hispanic White (n = 6539, 78.6%) or non-Hispanic Black (n = 1776, 21.4%) who were attending either suburban (n = 5188, 62.4%) or urban (n = 3127, 37.6%) schools. The outcome was standard math and reading grades. The independent variable was parental educational attainment. Gender, parental marital status, and school characteristics (% free lunch and relationship quality with the teacher) were the confounders. Race/ethnicity was the effect of modifier. School urbanity was the strata. Linear regression was used for data analysis.

In urban and suburban schools, higher parental educational attainment was associated with higher math and reading test scores. In urban and suburban schools, Black students had considerably lower reading and math scores than White students. At urban but not suburban schools, significant interactions were found between race (Non-Hispanic Black) and parental education attainment (years of schooling) on reading and math scores, suggested that the protective effect of parental education on students’ reading and math scores (ie school achievement) is smaller for Non-Hispanic Black relative to Non-Hispanic White youth only in urban but not sub-urban schools.

Diminished returns of parental education (MDRs) contribute to the racial achievement gap in urban but not suburban American high schools. This result is important given Black students are more likely to attend urban schools than White students. As MDRs are not universal and depend on context, future research should study contextual characteristics of urban schools that contribute to MDRs.

Introduction

Educational success plays an important role in shaping people’s future opportunities in life. 1 Students who perform better at school are more likely to gain higher salaries, 2 become active citizens, 3 experience higher life satisfaction, 4 and avoid high risk and criminal behaviors 5 during adulthood. Given that students’ academic achievement (eg test scores) is a substantial predictor of economic and non-economic outcomes later in life, 5 it is necessary to address racial inequalities in school performance, also known as the Black-White achievement gap.

Previous research has shown persistent and large racial inequalities of academic achievement in the United States school system, which is in part due to the low quality of education at urban schools. 6 Urban schools provide a lower quality of education because of fewer resources, leading to lower academic achievement among students. 7 The Black-White achievement gap, however, remains significant beyond controlling for school urbanity as a covariate. 8 It is, however, unknown whether Black-White achievement gap can be seen within both urban and suburban schools.

Child’s academic achievement is affected by school socioeconomic status (SES). 9 Concentration of poverty among students who attend urban schools is considered one of the greatest contributors to low academic achievement in such contexts. 10 Recent studies demonstrate that poverty has both a direct impact on student achievements through cognitive ability 11 and education quality. 12 For example, differential teacher quality may explain some of the achievement gap between Blacks and Whites. 13 Children of low SES families start school less prepared 14 and academically perform worse than their peers with higher family SES at school. 10 This is another reason we need to differentially study contributors of the Black-White gap in predominantly Black (suburban) and White (suburban) schools.

Family SES is also among the major determinants of students’ school achievement. 9 Among SES indicators, parental education is among the most stable aspects of family SES background, 15 which influence the quality of home learning settings 16 and higher levels of parental engagement in children’s schooling. 16 , 17 Children of low-educated parents more so than students with highly educated parents tend to demonstrate lower educational achievement. 18 Parents with higher education levels can provide greater SES resources which influences the academic achievement of their children, 19 but lower SES in Black parents can be restricted their investment in their children. 20

Eligibility for using free or reduced price lunch (FRPL) is one of the other indicators of SES that may predict student academic achievement. 21 FRPL is a proxy of school SES. 21 , 22 Predominantly Black and urban schools have higher FRPL usage than predominantly White and suburban schools. 23 Students who are FRPL eligible score lower in reading and math test scores than equivalent students who were not eligible for FRPL. 24

The educational attainment gap between students of low SES and high SES is observable at the age of seven, and at the end of compulsory education. 10 In addition, education aspiration, especially among poor students, has been connected to academic achievement, 25 such that over time and when approaching the end of compulsory education, educational aspirations fall in low-income students. 14

In the US, students with low family SES are most likely to attend urban schools. 26 On the other hand, children who go to urban schools in low-income neighborhoods show lower academic progress. 27 , 28 Indeed, sixth-grader children in the wealthiest school regions compared to children in the poorest regions are four grade levels ahead. 29 In addition, the high percentage of students with low family SES in urban schools is related to lowers the engagement and attempt of all students. 7

In the US education system, schooling and residential place are closely connected. As a result, residential neighborhoods affect both the value of property (wealth) and the racial and socioeconomic composition of schools. 30 Therefore, the role of race could be disadvantageous for the academic achievement of minorities. 23 In spite of the modest decreases in the Black-White residential segregation, Black students remain concentrated in racially segregated public schools in urban zones where a higher percentage of the population is from the racial or ethnic minority groups with low income. 31 A previous study showed schools in the poor regions have insufficient resources and elementary education quality varies drastically racial lines. 32 Also, research has shown lower academic achievement obtain in urban schools and schools where a greater part of the student body is Blacks. 31 , 33

Race is closely linked to family SES. 34 Race is also associated with school SES and racial composition. 35 Schools with a high concentration of minorities have lower SES. 36 School social status, student–teacher ratio, class size, teacher selection, and school rules are influenced by school SES. 23 African American students in mainly Black schools both in advanced and general classes are more likely to be exposed to a less demanding curriculum due to lower teacher quality and expectations 31 which lead to the academic achievement gap.

Furthermore, a significant factor in enhancing student educational achievement is teacher quality. 37 Educational background, professional certifications, and teaching experience of teachers improve student achievement in schools. 38 Studies in the US have shown that students’ academic achievement may be three times higher when taught by a high-quality teacher. 39 But evidence indicates that schools with a high percentage of minority students are more likely to have lower qualified and experienced teachers with higher rates of teachers’ turnover. 40 , 41 Also, new teachers inclined to start their work in schools with greater concentrations of low SES students do so to acquire more experience and then tend to transfer to higher-SES schools. 30 In addition, poor and Black students are more probably taught by teachers who have an alternative certification. 6

Although Black and White students' academic achievement gap improved across time, 42 this gap still exists and substantial especially in urban schools. 6 , 43 Recent data revealed the achievement gap between White and Black youth in reading and math remained relatively constant over the past years. 44 Additionally, the Black-White academic achievement gap in both reading and math scores during the first 4 years of the primary school nearly 0.10 standard deviations increased each year. 45 Research suggests this gap may be due to the SES achievement gap between minorities and the majority group. 46 However, in recent years, the relationship between race and SES also is dropping. 47

Research has revealed that racial/ethnicity minorities may not receive equal gains from their SES compared to dominant racial/ethnic groups, 48 , 49 identified as Marginalization-related Diminished Returns (MDRs). 50 , 51 In the US, marginalization-related diminished returns of SES indicators (eg education attainment, income) are established across domains of health 52 such as exercise, 53 diet, 54 smoking, 55 , 56 depression, 57 , 58 anxiety, 59 mental well-being, 60 self-rated mental health, 61 and chronic medical conditions. 54 Recent research has also documented diminished returns of parental educational attainment on school performance of Black children. 62

Recently, diminished returns of parental educational attainment have been investigated on student academic performances of minorities group in the US population. In two studies, parental educational attainment has smaller positive effects on grade point average (GPA) in Hispanic and Black than non-Hispanic White youth 62 and non-Hispanic Blacks than non-Hispanic Whites college students. 63 The result of another study indicated that the family socioeconomic position at birth has a smaller protective result for school bonding in Black than White youth. 64

To investigate diminished returns of parental education on the school performance of elementary students, we applied a nationally representative sample to compare Black and White students reading and math scores as a function of race and parental education. We tested these effects across urban and suburban schools. Our hypothesis is that Black and White students’ academic performance are differently affected by parental education and available resources in urban and suburban schools. If we could observe diminished returns of parental education in both urban and suburban schools, then diminished returns of parental education is not just a problem of urban schools. MDR are also not all because Black and White students attend different schools. If diminished returns exist in urban schools but not in suburban schools, urban school would be the context that generates MDR. We hypothesized that urban schools (low education quality and poor education context) not suburban schools will show MDR. That means we expect urban schools to be a mechanism by which parental education generates less outcomes for Black than White students.

Design and Settings

This cross-sectional study is a secondary analysis of Wave 1 of the Education Longitudinal Study (ELS) study. 65 Funded by the US Department of Education (DOE), ELS is one of the main studies of youth education in the US. The ELS sample is representative of 10 th grade youth in the US. Although ELS has enrolled all race/ethnic groups, this analysis was limited to 10,702 youth who were composed of 2020 (18.9%) non-Hispanic Black and 8682 (81.1%) non-Hispanic White youth. Exclusion criterion in the current analysis was any race other than White or Black.

All participating youth provided written assent. Youth parents signed written consent. The institutional review board (IRB) of the Department of Education (DOE) approved the protocol of the original article. As this analysis used fully deidentified public data, this secondary analysis was deemed to be exempted from a full IRB review.

Sample and Sampling

The ELS youth sample was enrolled in the private, public, or Catholic schools. These schools could be selected from either Urban, Suburban, or Rural areas. ELS used a multi-stage stratified random sample. The analytical sample of this study was 10,702.

Study Variables

The study variables included race/ethnicity as the moderating variable, parental education as the independent variable (predictor), youth math and reading test scores as the outcomes (dependent variables), and demographic factors (gender and parental marital status) and school characteristics (% students receiving free lunch).

Race/ethnicity

Race/ethnicity, a dichotomous variable, was self-identified as non-Hispanic Black (1) versus on-Hispanic White (0).

Parental Educational Attainment (Independent Variable)

Parental educational attainment was a continuous variable that reflected the highest number of years of schooling in parents. This variable was self-reported by the parents.

Demographic Factor (Covariates)

Gender and family structure were demographic covariates. Parental marital status was a dichotomous variable (1 = married parents, 0 = unmarried parents) and calculated based on parents’ marital status. Gender was 1=male 0 = female.

School characteristics

School characteristics included school control system (public versus private), and % students receiving free lunch. Both these variables were administrative rather than self-report.

School Urbanity

School urbanity was a dichotomous variable: 1= urban schools, 0 = suburban schools. This variable was administrative, not self-report.

School Function/Achievement (Outcomes)

Our dependent variables were standardized test scores of math and reading. These variables were transformed to z scores which helps comparison of the students and interpretation of the regression coefficients. Students performed the test.

Statistical Analysis

SPSS 23.0 (IBM Corporation, Armonk, New York, US) was used to analyze the data. We had normally distributed outcomes thus we decided to perform linear regressions. There was no multicollinearity between our independent variables such as race/ethnicity and parental educational attainment. Our model passed the assumption of homoscedasticity (eg, distribution of error terms). This strategy also helped us with the comparability of MDR across our two outcomes. We ran two hierarchical linear regression models per outcome, in the pooled sample. The first block of variables only included race/ethnicity. Our second block included educational attainment (years of schooling). Block 3 included the educational attainment (years) by race/ethnicity interaction term. The fourth block included gender and parental marital status. The fifth block included school characteristics, namely, relation with the teacher and % students with free lunch. We reported beta (b), B, standard error (SE), and p value.

Descriptive Data

Table 1 provides descriptive statistics for our sample. This study included 8315 youth. From this number, most were non-Hispanic White (n = 6539, 78.6%) and a minority were non-Hispanic Black (n = 1776, 21.4%). From all participants, most were attending either suburban schools (n = 5188, 62.4%) and a minority were attending urban schools (n = 3127, 37.6%).

Descriptive Characteristics

Qualitative Variables	All n = 8315		Suburban n= 5188		Urban n = 3127
Qualitative Variables	n	%	n	%	n	%
School Urbanity
Suburban	5188	62.4	5188	100.0	0	0
Urban	3127	37.6	0	0	3127	100.0
Race (Black) *
Non-Hispanic White	6539	78.6	4386	84.5	2153	68.9
Non-Hispanic Black	1776	21.4	802	15.5	974	31.1
Gender
Female	4204	50.6	2631	50.7	1573	50.3
Male	4111	49.4	2557	49.3	1554	49.7
Parental Marital Status*
Not- Married	3325	40.0	1992	38.4	1333	42.6
Married	4990	60.0	3196	61.6	1794	57.4
Quantitative variables
Parental education (years) *	4.81	2.00	4.66	1.99	5.05	2.01
Teacher student relation*	0.34	0.74	0.30	0.72	0.42	0.78
% Free Lunch	2.86	1.89	2.89	1.77	2.83	2.07
Test Score (Reading)	51.82	9.83	51.76	9.62	51.91	10.16
Test Score (Math) *	51.57	9.73	51.79	9.45	51.21	10.17

Note : * p < 0.05.

Bivariate Analysis

Table 2 presents the summary of bivariate analysis in the pooled sample. Reading and math test scores were closely correlated (r = 0.75, p < 0.001). % free lunch at school was inversely correlated with reading (r = −0.33, p < 0.001) and math (r = −0.36, p < 0.001) scores. Urban school was correlated with lower math (r = −0.03, p < 0.001) but not reading (p > 0.05) score. Race (Black) was negatively correlated with both reading (r = −0.34, p < 0.001) and math (r = −0.39, p < 0.001) scores.

Correlations in the Pooled Sample (n=8315)

	1	2	3	4	5	6	7	8	9
1 School Urbanity (Urban)	1	0.19**	0.09**	0.00	−0.04**	0.08**	−0.02	0.01	−0.03**
2 Race (Black)		1	−0.13**	−0.00	−0.30**	−0.06**	0.39**	−0.34**	−0.39**
3 Parental education (years)			1	0.01	0.15**	0.09**	−0.31**	0.33**	0.34**
4 Gender (Male)				1	0.02	−0.00	0.00	−0.07**	0.07**
5 Parental Marital Status (Married)					1	0.09**	−0.25**	0.23**	0.25**
6 Teacher student relation						1	−0.12**	0.13**	0.14**
7% Free Lunch							1	−0.33**	−0.36**
8 Test Score (Reading)								1	0.75**
9 Test Score (Math)									1

Note : ** p < 0.01.

Multivariable Models

Table 3 presents the summary of two hierarchical linear regression models in suburban schools. In both these models, race (non-Hispanic Black) and parental educational attainment were associated with the outcomes (reading and math scores). These models did not show statistical interactions between parental educational attainment and race on youth educational outcomes. No interaction terms suggest that the link between high parental educational attainment and youth educational outcomes (grades) is similar for non-Hispanic Black than and Non-Hispanic White youth.

Associations Between Race and Parental Education on Reading and Math Score at Suburban Schools

	B (Std. Error)	B (Std. Error)	B (Std. Error)	B (Std. Error)	B (Std. Error)
Reading
Race (Black)	−7.90(0.42)***	47.32(0.38)***	−6.41(0.96)***	−5.77(0.96)***	−5.10(0.96)***
Parental Education	–	−7.16(0.40)***	1.35(0.08)***	1.30(0.08)***	1.16(0.08)***
Black* Education (Years)	–	–	−0.17(0.20)	−0.17(0.20)	−0.18(0.20)
Gender (Male)	–	–	–	−1.99(0.28)***	−2.01(0.28)***
Parental Marital Status	–	–	–	2.06(0.30)***	1.77(0.30)***
Teacher student relation	–	–	–	–	0.95(0.20)***
% free lunch	–	–	–	–	−0.50(0.09)***
Math
Race (Black)	−9.04(0.40)***	−8.25(0.38)***	−7.04(0.92)***	−6.42(0.91)***	−5.67(0.91)***
Parental Education	–	1.41(0.07)***	1.46(0.07)***	1.38(0.07)***	1.24(0.08)***
Black* Education (Years)	–	–	−0.28(0.19)	−0.27(0.19)	−0.28(0.19)
Gender (Male)	–	–	–	1.13(0.27)***	1.11(0.27)***
Parental Marital Status	–	–	–	2.16(0.29)***	1.85(0.29)***
Teacher student relation	–	–	–	–	0.97(0.19)***
% free lunch	–	–	–	–	−0.56(0.08)***

Note : ***p < 0.001.

Table 4 presents the summary of two hierarchical linear regression models in urban schools. In both these models, race (non-Hispanic Black) and parental educational attainment were associated with the outcomes. These models showed an interaction between parental education and youth outcomes. These interaction terms suggest that the positive link between parental education and youth educational outcomes (grades) is smaller for non-Hispanic Black relative to Non-Hispanic White youth. These findings suggest that in urban schools, non-Hispanic Black youth with highly educated parents have low school performance, and these diminished returns of parental education.

Associations Between Race and Parental Education on Reading and Math Score at Urban Schools

	B (Std. Error)	B (Std. Error)	B (Std. Error)	B (Std. Error)	B Std. Error
Reading
Race (Black)	−9.45(0.42)***	−8.06(0.41)***	−4.94(1.03)***	−4.06(1.04)***	−5.10(0.96)***
Parental Education	–	1.36(0.09)***	1.54(0.11)***	1.48(0.11)***	1.16(0.08)***
Black* Education (Years)	–	–	−0.68(0.21)**	−0.66(0.20)**	−0.18(0.20)
Gender (Male)	–	–	–	−0.86(0.36)*	−2.01(0.28)***
Parental Marital Status	–	–	–	2.46(0.39)***	1.77(0.30)***
Teacher student relation	–	–	–	–	0.95(0.20)***
% free lunch	–	–	–	–	−0.50(0.09)***
Math
Race (Black)	54.99(0.21)***	47.87(0.52)***	46.56(0.59)***	44.33(0.65)***	47.13(0.73)***
Parental Education	–	−9.00(0.40)***	−4.91(1.00)***	−3.85(0.99)***	−2.51(0.99)*
Black* Education (Years)	–	–	1.57(0.10)***	1.51(0.10)***	1.29(0.10)***
Gender (Male)	–	–	–	−0.90(0.20)***	−0.87(0.19)***
Parental Marital Status	–	–	–	1.39(0.34)***	1.46(0.33)***
Teacher student relation	–	–	–	–	1.95(0.37)***
% free lunch	–	–	–	–	1.06(0.22)***

Notes : *p < 0.05; **p < 0.01; ***p < 0.001.

The current study showed four findings: First, high parental education was linked to higher math and reading scores among youth. Second, in urban but not suburban schools, the boosting effect of parental education on school achievement is weaker for non-Hispanic Black than for Non-Hispanic White families. Third, parental education differently influenced reading and math scores at urban schools above and beyond relation with children, marital status of the parents, and % free lunch eligibility of the students. Fourth, at suburban schools, Black and White students similarly gain school performance from their parental education.

Diminished returns of education contribute to the Black-White academic gap in urban schools only. At urban schools, Black students remain at educational risk, despite their highly educated parents. Such level of risk is unexpected and disproportionate to their parental educational attainment.

As MDR remained as a contributor to the Black-White achievement gap in urban but not suburban school characteristics, we argue that urban school context may be a probable cause for diminished returns of parental educational attainment as a cause of racial achievement gap. Still, as shown by previous studies, other upstream social forces that occur beyond education system may also contribute to such gap. For example, labor market discrimination may put Black parents in worse jobs, which may reduce Black parents’ available time to engage with their children’s school activities. However, differential context of education of Black and White youth seems to be essential for Black-White achievement gaps due to MDR. 66

With a similar pattern observed in urban schools, Black students with highly educated parents have shown to report poor school outcomes, 67 poor school attainment, 51 and poor school bonding; 68 all outcomes that are disproportionate to their high parental education. Previous research, however, could not tell whether these patterns can be observed in both urban and suburban settings.

One recent study documented worse than expected aggression, chronic diseases, tobacco use, psychological problems, and school performance in Black youth with high parental education. 66 As argued in other studies, 69 – 71 a plausible conclusion seems to be that some distal and upstream social processes interfere with the effects of parental education for racial and ethnic minority families. 66 According to this study, however, diminished returns of parental education are not all because Black youth attending urban schools where the quality of schooling is lower.

The education system seems not to be the only reason we see worse outcomes for Black youth in the middle class with highly educated parents. Diminished returns of parental and own education result in higher than expected prevalence of asthma, 72 Attention Deficit Hyperactivity Disorder, 73 mental health problems, 74 depression, 75 , 76 obesity, 61 , 77 dental health problems, 78 poor health-care use, 79 impulsivity control, 80 and cigarette smoking 81 in middle class Black families. Similar patterns are shown for Black adults, 82 – 84 Black youth, 77 , 80 , 85 Hispanic adults, 56 , 86 , 87 and Hispanic youth. 66 As similar patterns are shown for various groups, it is not a group behavior but the upstream underlying mechanisms such as social stratification, structural racism, and marginalization that reduce the positive effects of education for minority families. 66

While parental education promotes educational outcomes for youth, this association is diminished for Non-Hispanic Black and White youth. The smaller marginal returns of parental education are beyond what can be explained by school characteristics that differ between Non-Hispanic Black and non-Hispanic White students. Diminishing returns of parental educational attainment (MDR) may be an unrecognized source of racial youth disparities. Equalizing SES would not be enough for equalizing outcomes. There is a need for public and economic policies that reduce diminished returns of SES for Black families.

The major contribution of this study is that we found diminished returns of parental education in urban but not suburban schools. That is, diminished returns of parental education may be specific to the context, thus modifiable through educational policies. This results in advocates for policies that target the education quality of urban schools. We argue that Black youth from the middle class perform worse than expected in part because they are more likely to attend urban schools. They would probably do similar to White middle-class youth if they had the chance to attend suburban schools.

Racial inequalities and disparities are not all due to the lower SES of Blacks as inequalities can be also seen in middle-class people. Thus, other social mechanisms are at work to cause inequalities across racial groups, even for middle-class families that access education.

Policy Implications

These results have considerable implications. Innovative policies, as well as public health programs, should be designed, implemented, and evaluated to reduce racial and ethnic inequalities across all levels of SES strata. To address disparities that are not due to low SES but diminished return of SES, we should go beyond exclusively focusing on equalizing access to resources. While equal access is important, there is a need to address broader social and economic processes that hinder middle-class Black families’ abilities to leverage their available resources. Policies should aim to equalize the gain that follows access to SES resources. Such policies are hoped to reduce inequalities that sustain across all the SES spectrum. 55 , 59 , 77 – 80 , 82 , 86 , 88 , 89 We need policies and program solutions that equalize highly educated Black families’ abilities to leverage their educational attainment. 88 , 90 Some suspect the cause of MDR are labor market practices and preferences. 82 Although there are strong anti-discrimination laws, further enforcement of such existing policies may be required if we want to minimize the existing diminishing marginal returns of education and other SES indicators in the lives of Black families. Communities, where the majority of residents are Black, may benefit from higher quality and abundant jobs that facilitate translation of educational attainment into tangible real-life outcomes. 91 Programs should help highly educated Black parents successfully compete with Whites to secure high paying jobs. At the same time, we should reduce the societal and environmental barriers that are common in the everyday lives of Black population. In addition, we should invest in educational programs and in outcomes of Black youth, including middle-class Black youth, in urban and suburban schools. There is a need to empower Black parents to utilize their education and increase their engagement with their children's school performance. Finally, we need to minimize the discrimination of Black students in urban and suburban schools. 92 , 93

Limitations

This study had a few limitations. With our cross-sectional design, we cannot make any causal inferences. The unequal sample size across groups prevented us from running race-specific models. This study only included Non-Hispanic Blacks and Non-Hispanic Whites. Other ethnic minorities such as Hispanics, Asians, and Native Americans should be included in future studies. We only studied the differential effect of parental educational attainment. Other family SES indicators such as wealth, income, employment, and area-level SES should be studied. This study did not include geocoded data. Thus, educational policies were not included. Despite these limitations, this study still contributes to the MDR literature on as well as the racial achievement gap. Some strengths included a large sample size, a random sample, and a representative sample that resulted in generalizable findings to the US, and standardized tests. However, the main contribution of this study is beyond these methodological strengths. This was the first study showing that diminished returns of parental education on school performance can be seen in both urban and suburban schools, thus these diminished returns are not all because of poor urban schools that are weak in educational quality.

In the United States, non-Hispanic Black youth who attend urban schools are at a disadvantage compared to Non-Hispanic White youth regarding the magnitude of the effect of parental education on their educational outcomes. Such diminished returns of parental education that are observable in urban schools are absent in suburban schools. Context of education may be one reason Black youth from middle-class families do worse than middle-class White youth.

The authors report no conflicts of interest in this work.

Kellogg School of Management at Northwestern University

Social Impact Policy Mar 1, 2021

There’s an education gap between rural and urban communities. can technology bridge it, researchers identified a program that helps rural students learn—and improves their incomes later in life..

Nicola Bianchi

Yevgenia Nayberg

Where a child is born has enormous influence over their educational future.

Even within nations, there tends to be a yawning gap between urban and rural education outcomes. For instance, according to one 2015 standardized assessment, 15-year-olds studying in urban schools in 37 countries outperformed rural students by roughly the equivalent of one full year of schooling, even after controlling for students’ socioeconomic backgrounds.

Many of the solutions intended to narrow this urban–rural gap rely on technology—with a particular focus on tech tools that can help connect far-flung students to quality educators. But are these technologies really up to the challenge?

Most previous research on this question has focused on short-term outcomes, like the immediate effects on students’ test scores, notes Nicola Bianchi , assistant professor of strategy at the Kellogg School.

In a new study, however, Bianchi and coauthors Yi Lu , at Tsinghua University in Beijing, and Hong Song , at Fudan University in Shanghai, consider much-longer-term impacts: how much school rural students completed and what they went on to earn once they joined the workforce.

The researchers focused on China, a country with a particularly pronounced chasm between the quality of urban and rural education systems. In 2004, as part of an effort to address the disparity, the Chinese government started a program to connect over 100 million rural students with highly qualified urban teachers via satellite. Because of the large number of students involved, the Chinese program is likely the world’s largest-ever education-technology intervention, the researchers note.

Then, using data from a massive survey conducted a decade later, the team was able to analyze the long-term effects of this reform on students’ educational and career trajectories.

They found that rural Chinese students who had access to classes delivered by top teachers appeared to benefit in multiple ways that persisted over time. Specifically, those who had been exposed in middle school to lectures recorded by high-quality urban teachers ultimately completed more education than their peers and earned significantly more once they started working.

“Technology can be a fantastic way to bring high-quality education by some of the best teachers in the country to rural areas without trying to convince teachers to relocate,” Bianchi says. “In other words, when it comes to increasing the quality of education in these underserved areas, technology can be the channel through which we achieve that.”

Tracking Students Touched by an Educational Reform

The average rural student in China has long lacked access to the same quality of education as his or her urban peers. In 2000, a few years before China’s ambitious rural education project began, only 14 percent of rural middle-school teachers held a bachelor’s degree—less than half of the percentage among their urban counterparts. Rural schools also had larger class sizes than urban ones and often lacked necessary teaching materials.

This appeared to affect students’ trajectory after middle school. Only 7 percent of rural Chinese middle-school students went on to enroll in high school; among urban students, high-school enrollment was over nine times higher.

“The exposure to the education technology allowed [rural students] to escape the most common job in very rural parts of China, which is working in agriculture.” — Nicola Bianchi

To lessen this divide, the Chinese Ministry of Education in 2004 embarked on a four-year project to install satellite dishes, computer rooms, and other multimedia equipment in the country’s rural schools. It also sought the highest-credentialed teachers in the country to record lectures that rural students could access via the internet and DVDs. (Most of those teachers came from selective urban elementary and middle schools.)

The researchers estimate that the average rural student watched roughly seven 45-minute lectures per week. Importantly, the students watched the lectures not from their own homes, but in school classrooms, under the supervision of local teachers.

To analyze the long-term impacts of these technological interventions, the researchers turned to the 2014 China Family Panel Studies, a representative survey of Chinese communities, families, and individuals conducted by Peking University. Of particular interest to Bianchi and his coauthors were respondents’ age, educational attainment, and earnings. Also, crucially, the survey asked respondents where they lived at age 12, which allowed the researchers to ascertain if their middle school benefitted from the new educational technology during their time there.

Shifting Educational and Employment Futures

The researchers’ analysis revealed that the Chinese government’s ambitious program did discernibly benefit rural students—not only academically, but in the job market as well.

Rural students with access to the government’s computer-assisted learning program completed 0.85 years of additional schooling compared with those without access. And remarkably, nearly a decade after their time in middle school, these rural students also earned 59 percent more than peers in the same county not touched by the reform.

“What was interesting was that it was not just an earnings increase, but a difference in type of occupations,” Bianchi says. “The exposure to the education technology allowed them to escape the most common job in very rural parts of China, which is working in agriculture. They were moving away from these jobs and towards jobs that were more focused on cognitive skills.”

Bianchi and his coauthors conclude that exposure to the program accounted for a 21 percent reduction in the preexisting urban–rural education gap and a 78 percent reduction in the earnings gap.

The program also furnished rural schools with the ability to introduce computer-science classes and the means for rural teachers to incorporate computers into their own lectures. Yet the researchers point to the recorded lectures by the highly credentialed teachers as the standout star in terms of their impact on the students. The other technologies, they write, “are not corroborated by data and anecdotal evidence” as discernibly benefiting students.

Narrowing a Persistent Regional Divide

So the technology initiative had a significant, positive impact on the students. Does this translate to benefits for students around the globe who are using technology to learn remotely during COVID-19? Bianchi says it likely doesn’t.

It’s important to remember, he says, that the Chinese reform placed students in a learning context quite different from the living rooms and kitchen tables that most virtual students are dealing with today.

“When we generally talk about remote learning, we think about students by themselves at home, sometimes without any type of supervision, taking or following a class,” he says. “The Chinese example was very different because the students were in class and they were under the direct supervision of the local teachers.”

Bianchi notes that he expects a wide variety of sectors to embrace a remote format even after the pandemic is over—but he doesn’t expect education to be one of them. There are simply too many clear benefits of in-person learning.

“But that doesn’t mean technology can’t help rural areas get access to something that they wouldn’t have, even in person,” he says.

Assistant Professor of Strategy

About the Writer Katie Gilbert is a freelance writer in Philadelphia.

Read the original

We’ll send you one email a week with content you actually want to read, curated by the Insight team.

Undergraduate Admission
Graduate Admission
Tuition & Financial Aid
Communications
Health Sciences and Human Performance
Humanities and Sciences
Music, Theatre, and Dance
IC Resources
Office of the President
Ithaca College at a Glance
Awards and Accolades
Five-Year Strategic Plan
Public Health
Directories
Course Catalog
Undergraduate

The Education Gap (or Achievement Gap or Opportunity Gap) and Models of Effective School Reform

The Education Trust -- a national organization devoted to research and action to address the education/opportunity gap. Their research reports on both issues/problems AND successful schools and effective educational strategies. This site provides lots of excellent resources.

Whither Opportunity? Rising Inequality, Schools and Children's Life Chances -- information and an executive summary about a 2011 book that "illuminates the ways rising inequality is undermining one of the most important goals of public education -- the ability of schools to provide children with an equal chance at academic and economic success."

The Nation’s Report Card -- This website is full of charts and figures on 2008’s reading and mathematics scores. You can isolate data from a variety of categories of student scores including student age level (ages 9, 13, and 17), race, social class, gender, and parent education.

Education Gap Grows Between Rich and Poor, Studies Say -- This article from the New York Times discusses the way that, despite the achievement gap between white and black students slowly decreasing over time, the gap between poor and wealthy students is drastically increasing. It also discusses possible reasons for this achievement gap.

Trends in High School Dropout and Completion Rates -- lots of useful date from the 1980's through 2008.

High School Graduation Rates in the United States -- a report of 4-year high school completion rates for the class of 1998 -- very distressing numbers.

H igh School Graduation Rates Unacceptably Low, State Says -- an article about 2005 4-year high school graduation rates in New York State and City.

Low Income Hinders College Attendance Even for the Highest Achieving Students -- a report, with an excellent graph, that provides evidence of how low income students are less likely to attend college, even with high academic achievement.

Mind the Gap: Why Good Schools are Failing Black Students -- an excellent 2009 radio documentary about how in many well-funded suburban schools where white students are doing well, many black and Hispanic students, even youth from middle-class families, are falling behind. This one-hour radio documentary looks at the causes of the minority achievement gap through the stories of students, teachers, and parents at a diverse public high school in Maplewood, NJ.

Closing the Racial Achievement Gap: The Best Strategies of the Schools We Send Them To -- a good article by Dr. Pedro Noguera, who has done extensive research on this issue.

Yes We Can: Telling the Truths and Dispelling the Myths About Race and Education in America -- a 2006 report that examines the educational practices and policies that have raised academic achievement for low-income and minority students, and offers compelling evidence that children of color excel in school when given the right teaching, right classes, and right support.

Middle Class and Marginal? -- Four studies are used to illustrate the way that even students from middle-class homes may be negatively influenced by their socioeconomic backgrounds when surrounded by peers of even higher SES at elite universities.

Boundary Crossing for Diversity, Equity and Achievement: Inter-District School Desegregation and Educational Opportunity -- a 2009 study that "provides an overview of the educational and social benefits of eight inter-district school desegregation programs – from Boston to East Palo Alto, CA -- that have enabled disadvantaged, Black and Latino students to cross school district boundary lines and attend far more affluent, predominantly White and privileged suburban public schools. These programs, some of which date back to the Civil Rights Movement, grew out of grassroots struggles for social justice and are aimed at reducing inequality by assuring that students who have traditionally had the fewest educational opportunities would gain access to the “best” schools. Despite the fact that these programs are out of sync with the current political framing of problems and solutions in the field of education, the research on these programs to date suggests that they are far more successful than recent choice and accountability policies at closing the achievement gaps and offering meaningful school choices."

Teaching Tolerance -- This website offers a collection of classroom resources, professional development programs, and magazine and publication section. It seeks to act as a medium for the educational community in order to educate for a diverse democracy.

Edutopia -- an organization committed to identifying and supporting practices and programs in public education that work, with an emphasis on global and project-oriented learning -- the site includes "hundreds of exemplary programs and smart practices."

The Harlem Children's Zone -- a radio report about Geoffery Canada's ambitious and hopeful reform project in Harlem, New York City.

The Harlem Children's Zone Website -- detailed information about Geoffery Canada's reform organization, which implements the attitude, "whatever it takes" to prove that "poor, black children can and do succeed."

Closing the Racial Achievement Gap: The Best Strategies of the Schools We Send Them To -- a good article by Harvard Professor Dr. Pedro Noguera.

New Leaders -- New Leaders is an organization dedicated towards improving student learning through "developing transformational school leaders and advancing policies and practice."

Closing the Achievement Gap: Two Views from Current Research -- a 2003 discussion of research about the experiences of African American and Latino students in suburban schools -- includes discussion of work by now deceased UC Berkeley Prof. John Ogbu, about the experiences of African American students in Shaker Heights, OH, and by Harvard Prof. Ron Ferguson.

The Trouble with Black Boys: The Role and Influence of Environmental and Cultural Factors on the Academic Performance of African American Males -- a very good article by scholar Pedro Noguera -- valuable for all who are interested in addressing the education/achievement gap.

Thin Ice: "Stereotype Threat" and Black College Students -- another valuable article by Stanford Professor, Claude Steele, from 1999 -- explains the concept of stereotype threat and related research -- very useful to educators and teachers.

The Village at Ithaca -- a community-based organization of concerned citizens who are working in Ithaca, NY to eliminate the education gap.

Webster Groves Writing Project -- a successful multicultural approach to the teaching of writing -- this program has been written about in a number of books and articles about effective use of culture in designing and delivering instruction.

AVID -- a program that "places academically average students in advanced classes and supports them for success there" -- it has been very successful as measured by college admission rates of program participants, most of whom are students from groups with a history of high dropout rates and underperformance in school.

The Algebra Project -- an exceptional math education program, now nationally recognized, created by Civil Rights activist and Harvard Ph.D., Bob Moses -- a creative and culturally responsive approach to teaching African American and other youth algebra -- algebra is a major gatekeeping discipline and course that often determines whether youth are placed on the college prep path -- the program works, and the website includes lesson ideas and other useful information.

The Preuss School -- a charter middle and high school dedicated to providing a rigorous college prep education for motivated low-income students who will become the first in their families to graduate from college -- affiliated with the Univ. of California, San Diego.

Inner City School Founder: No Miracle, Just Teaching -- a radio program segment about Paul Adams who took a once Catholic school from the brink of being closed to becoming a very successful non-profit independent school that sends 100% of its inner city students to college. From the Providence St. Mel website: "To score highly on standardized tests, our faculty helps students build their critical thinking and problem solving skills."

The Capstone Institute at Howard University -- "Capstone Institute is a multi-disciplinary center that implements and supports school reform and school improvement initiatives that focus on "educating the whole child," and interlinks research, theory and practice in the areas of learning, curriculum and instruction, professional development, social work, policy, parent and community engagement, organizational change, assessment and evaluation, and psychosocial/emotional development."

KIPP Schools (Knowledge is Power Program ) -- a model school program achieving significant success with children of color and limited resources who are so often relegated to inferior schools and education -- based on a combination of traditional and progressive educational ideas and begun by two grads of the Teach For America program.

Uncommon Schools -- a charter school organization that is achieving significant success in addressing the education gap -- students at their North Star Academy, in Newark, NJ, (most of whom are students of color who recieve free and reduced lunch) outperform students statewide on standardized tests and go on to college at very high rates (100% in 2006).

The Harvard Family Research Project -- a project founded on the belief that "for children and youth to be successful, there must be an array of learning supports around them. These supports, which must reach beyond school, should be linked and work toward consistent learning and developmental outcomes for children from birth through adolescence. Examples of nonschool learning supports include early childhood programs, families, after school programs, libraries, and other community-based institutions."

Leadership Enterprise for a Diverse America -- "Working in conjunction with high schools, Leadership Enterprise for a Diverse America (LEDA) seeks to identify promising high school juniors whose socio-economic, racial, and/or ethnic background is currently under-represented at the nation's top colleges and universities. LEDA's goal is to guide these students through the college application process, prepare them for the college experience and position them for leadership in the private and public sectors."

Educating Young Minds -- an exciting program in Los Angeles. "Educating Young Minds is a non-profit learning center that has been helping inner-city school children, ages 5-18, excel at school and at life since 1987. With “home-study” instruction during the day, after-school tutoring, and basic skills and standardized test preparation classes on Saturdays, Educating Young Minds is a vigorous program that serves students who are considered under-represented or at high risk in our society. Educating Young Minds also supports the progressive student who desires advanced academic support."

The SEED Foundation -- an interesting charter public school idea in Washington, DC, that involves students living at the school during the week. In both 2004 and 2005 100% of SEED School graduates went to college.

Closing the Achievement Gap -- a documentary film about Amistad Academy, a charter school founded in 1999, with the goal of "closing the persistent and dramatic achievement gap between minority students and white students in America's public school system."

The SAGE Program -- The Student Achievement Guarantee in Education (SAGE) program has been proven to improve student achievement in schools serving low-income communities. It does this through a reduction in class size, longer school hours, collaboration with community organizations, rigorous curriculum and high standards for teachers. When compared with the use of vouchers, this program produces higher levels of achievement through school reform rather than school choice.

The New Roots School -- a charter school in Ithaca, NY that is "committed to sustainability education and social justice".

No Child Left Behind -- the official government website for and about this federal school reform legislation.

John Hunter: Teaching with the World Peace Game -- a TED Talk by 4th grade teacher John Hunter about an incredible role play project he uses with his students to develop skills of critical analysis, reasoning, emotional intelligence, and much more. A very inspiring video and an amazing teacher and man!

Some Good Books and Articles

Baker, Melissa, and Pattie Johnson. 2010. The Impact of Socioeconomic Status on High Stakes Testing Reexamined. Journal of Instructional Psychology .

Barton, P. 2004. Why Does the Gap Persist? Educational Leadership, 62(3):8-13.

Caldas, Stephen J., and Carl Bankston. 1997. Effect of School Population Socioeconomic Status on Individual Academic Achievement. T he Journal of Educational Research.

Chenowith, K. 2007. "It's Being Done": Academic Success in Unexpected Schools . Harvard Universtiy Press.

Chenowith, K. 2009. How It's Being Done: Urgent Lessons from Unexpected Schools . Harvard Education Press.

Chenowith, K. 2010. Leavning Nothing to Chance: Principals from High Performing, High Poverty, and High Minority Schools Discuss What It Takes to Ensure that All Students Achieve. Educational Leadership , 68(3), 16-21.

Comer, J., et al., 1996. Rallying the Whole Village: The Comer Process for Reforming Education . Teachers College Press.

Conchas, G. 2006. The Color of Success: Race and High Achieving Urban Youth . Teachers College Press.

Delpit, L. 1995. Other People's Children: Cultural Conflict in the Classroom. The New Press.

DeRoche, T. 2004. Not Just a Necessary Evil: When Teachers Embrace Standards and Testing. Education Week.

Educational Leadership, 2004. The entire issue of Educational Leadership , November, 2004 (#62, v.3) is devoted to "Closing the Achievement Gaps."

Educational Leadership, 2006. The entire issue of Educational Leadership , February, 2006 (#63, v. 5) is devoted to "Helping Struggling Students."

Espinoza-Herold, M. 2003. Issues in Latino Education: Race, School Culture, and the Politics of Academic Success . Allyn and Bacon.

Ferguson, R. 2007. Toward Excellence with Equity: An Emerging Vision for Closing the Achievement Gap . Harvard Education Press.

Finnan, C. & Swanson, J. 2000. Accelerating The Learning of All Students: Cultivating Culture Change in Schools, Classrooms, and Individual s. Westview Press.

Gay, G. 2010. Culturally Responsive Teaching . Teachers College Press.

Gonzalez, M. et al., (Ed.) 1998. Educating Latino Students: A Guide to Successful Practice. Technomic Publishing.

Irons, P.. 2002. Jim Crow's Children: The Broken Promise of the Brown Decision . Penguin Group.

Kunjufu, J. 1997. Motivating and Preparing Black Youth for Success . African American Images.

Ladson-Billings, G. 1994. The Dreamkeepers: Successful Teachers of African American Children . Jossey-Bass.

Lewis, A. 2004. Washington Commentary: Redefining "Inexcusable." Phi Delta Kappan

Mehan, H. et al., 1996. Constructing School Success: The Consequences of Untracking Low-Achieving Students. Cambridge Univ. Press.

Morris, V. & Morris, C. 2000. Creating Caring and Nurturing Educational Environments for African American Children . Bergin and Garvey/Greenwood Publishing.

Moses, R. 2001. Radical Equations: Civil Rights from Mississippi to the Algebra Project. Beacon Press.

Nasir, N. & Cobb, P. (Eds.) 2007. Improving Access to Mathematics: Diversity and Equity in the Classroom . Teachers College Press.

Noguera, P. 2003. City Schools and the American Dream: Reclaiming the Promise of Public Educatio n. Teachers College Press.

Noguera, P & Wing, J. (Eds.). 2006. Unfinished Business: Closing the Racial Achievement Gap in Our Schools . Jossey-Bass.

Perry, T., Steele, C., & Hilliard, A. 2003. Young, Gifted and Black: Promoting High Achievement among African-American Students . Beacon Press.

Price, H.B.. 2002. Achievement Matters: Getting Your Child the Best Education Possible . Kensington Publishing Corp

Reyes, P. et al., (Eds.), 1999. Lessons from High Performing Hispanic Schools: Creating Learning Communities . Teachers College Press.

Roach, R. 2001. Gaining New Perspectives on the Achievement Gap (Algebra Project, Math and Science Literacy). Black Issues in Higher Educatio n, 18(1).

Slavin, R. & Calderon, M. 2001. Effective Programs for Latino Students . Lawrence Erlbaum.

Thernstorm, A., Thernstorm, S.. 2003. No Excuses: Closing the Racial Gap in Learning . Simon & Schuster.

Thompson, G.. 2009. A Brighter Day: How Parents Can Help African American Youth . African American Images.

Trumball, E. 2001. Bridging Cultures Between Home and School: A Guide for Teachers, with a Special Focus on Immigrant Latino Families . Lawrence Erlbaum.

Valdes, G. 2001. Learning and Not Learning English: Latino Students in American Schools . Teachers College Press.

Valencia, R. (Ed.) 1991. Chicano School Failure and Success . Falmer Press.

Valenzuela, A. 1999. Subtractive Schooling: U.S.-Mexican Youth and the Politics of Caring . SUNY Press.

Walpole, M. 2007. Social Class Effects and Multiple Identities. Economically and Educationally Challenged Students in Higher Educatio n.

Walsh, C. 1996. Pedagogy and the Struggle for Voice: Issues of Language, Power and Schooling for Puerto Ricans . Bergin & Garvey.

Welch, O. 1997. Standing Outside on the Inside: Black Adolescents & the Construction of Academic Identity . SUNY Press.

*************************************

Zero-tolerance policies lack flexibility -- This article explores different zero tolerance policies regarding weapons, alcohol and drugs. It argues that extreme policies are not the answer to dealing with unwanted conduct in schools.

Zero Tolerance Policies: Are the Schools Becoming Police States? -- In this article John Whitehead goes into detail about why teachers and officials do have a choice when it comes to punishing a student under a zero tolerance policy.

You are using an outdated browser. This website is best viewed in IE 9 and above. You may continue using the site in this browser. However, the site may not display properly and some features may not be supported. For a better experience using this site, we recommend upgrading your version of Internet Explorer or using another browser to view this website.

- Download the latest Internet Explorer - No thanks (close this window)

Penn GSE Environmental Justice Statement
Philadelphia Impact
Global Initiatives
Diversity & Inclusion
Catalyst @ Penn GSE
Penn GSE Leadership
Program Finder
Academic Divisions & Programs
Professional Development & Continuing Education
Teacher Programs & Certifications
Undergraduates
Dual and Joint Degrees
Faculty Directory
Research Centers, Projects & Initiatives
Lectures & Colloquia
Books & Publications
Academic Journals
Application Requirements & Deadlines
Tuition & Financial Aid
Campus Visits & Events
International Students
Options for Undergraduates
Non-Degree Studies
Contact Admissions / Request Information
Life at Penn GSE
Penn GSE Career Paths
Living in Philadelphia
DE&I Resources for Students
Student Organizations
Career & Professional Development
News Archive
Events Calendar
The Educator's Playbook
Find an Expert
Race, Equity & Inclusion
Counseling & Psychology
Education Innovation & Entrepreneurship
Education Policy & Analysis
Higher Education
Language, Literacy & Culture
Teaching & Learning
Support Penn GSE
Contact Development & Alumni Relations
Find a Program
Request Info
Make a Gift
Current Students
Staff & Faculty

Search form

You are here, rethinking the achievement gap.

By Andy Porter

Visit Porter's Center on Standards, Alignment, Instruction, and Learning (C-SAIL)

Back in the 1960s, the noted sociologist Christopher Jencks called for income tax redistribution to address the issue of racial inequality. Today, he looks to education: “Reducing the test score gap is probably both necessary and sufficient for substantially reducing racial inequalities in education attainment and earnings.”

Jencks is not alone in this assessment. In the last 40 years, more has been written about the achievement gap than just about any other topic in education. But what exactly is the achievement gap? How important is it? What has been done, and what can be done, to address it?

What Is the Achievement Gap?

The achievement gap is the persistent disparity in academic achievement between minority and disadvantaged students and their white counterparts. To begin my discussion of the issue, I feel I must in some way account for the nature-nurture tension that sometimes underpins conversations about the gap: suffice it to say that I weigh in with Richard Nisbitt, who stated that “[t]he most relevant studies provide no evidence of the genetic superiority of either race but strong evidence for substantial environmental contributions to the IQ gap between blacks and whites.”

In my view, it is not innate ability but rather the opportunity to learn—an artifact of environment—that underlies the achievement gap.

The best data available for looking at the achievement gap over time is the long-term trend data from the National Assessment of Education Progress (NAEP). A national probability sample, NAEP data are detailed by age group, not grade, and since the test itself has remained stable since the early 1970s, it paints a picture of how things have changed over time.

What do the data reveal? Consider just reading performance among nine-year-olds from the year 1971 to 1999. Clearly, the achievement gap did not narrow over this period: into the 1980s, some progress was made, but from that point on, the gap stabilized. The situation is basically similar for mathematics and not so very different for science.

In short, we have made progress—with the substantive improvements occurring early on—but not as much as we would like. It is instructive to note, however, that there are significant local variations: the gap reported in the state of Maine, for example, is much smaller (about one third of a standard deviation) than that in Wisconsin or Connecticut (both larger than one standard deviation).

Mind you, a gap that measures one standard deviation represents a serious disparity in achievement. Moving a child who lands at the middle of the distribution up by one standard deviation would move him roughly from the 50th percentile to the 84th percentile—a change that would delight any educator.

How Important Is the Achievement Gap?

The typical contrast used to define the achievement gap is the black-white divide or increasingly the Hispanic-white. But the gap could be defined by socio-economic status, and it could be criterion-referenced or norm-referenced. Because people talk about the achievement gap in these various ways, we need to be precise about what we mean.

When does the achievement gap begin? The gap between whites and blacks is present before children experience any schooling. By the time children are three or four, it is already a standard deviation.

Does the gap increase while students are in school? The surprising answer is no. Researchers have found that the rate of growth in achievement among blacks is equal to that among whites during the academic year. In the summertime, both groups show a decrease, but that decrease is larger for blacks than for whites. So while the achievement gap doesn’t increase while students are in school, it doesn’t decrease either.

Is the gap a function of test bias? No matter how hard they have looked, researchers have been unable to find any evidence of test bias. A number of people have hypothesized that administering performance assessments rather than multiple choice achievement tests would show a smaller gap, but this is not the case. In fact, an achievement gap of one standard deviation on multiple choice tests increases to 1.2 standard deviations with performance assessment. My hypothesis about this finding is that black children on average are not receiving the schooling they need to acquire the kind of knowledge needed to succeed in performance assessments. That is, there is differential distribution of opportunity to learn between black students and whites.

How are definitions of comparison groups changing discussions of the gap? I would venture that in ten or 15 years, we won’t be talking about the black-white achievement gap. Since the current Census allows respondents to report multiple ethnicities, we will have a much harder time in defining ethnic groups in the future. The achievement gap will still be there, and we will still worry about it. But we will likely be worrying about it in terms of socio-economic status.

What Solutions Have Been Tried?

Since the 1960s, attempted solutions to this problem have generally fallen into four different categories: preschool reforms, teacher reforms, instructional reforms, and standards-based reforms.

Preschool Reforms. Almost all of the research on preschool programs shows early gains in achievement, and that the early gains are not sustained. Moreover, the academic advantages of preschool programs are less likely to be sustained for children of color than for white children. We don’t know why, but the finding has been replicated many times.

But these programs vary tremendously in quality. The Perry Pre-school evaluation famously found that particular program to be massively successful, with participating students half as likely to go into special ed, five times less likely to be incarcerated, four times more likely to earn $2,000 or more monthly. But the sad truth is that not all programs are good programs and, to make matters worse, white students are more likely to participate in preschools than their black peers and the schools they attend are more likely to be of high quality.

Teacher Reforms. Education research has finally caught up with common sense in its understanding of teacher quality. For a long time, everybody knew that a good teacher was better than a bad teacher, but no one could actually document that teachers made any difference. Now, researchers have documented teacher effectiveness in raising student achievement.

Say a student—call him Johnny—has a good teacher every year, in the first grade, second grade, right up through the 12th grade. Let’s say that the good teacher has the effect of improving Johnny’s performance one tenth of a standard deviation. So that at the end of the first year, Johnny is a tenth of a standard deviation better off than he otherwise would have been. Now let’s say that the shelf life of that effect is perfect (Johnny keeps that advantage when he goes to second grade). In second grade, he improves another tenth of a standard deviation. By the time Johnny graduates from high school, he’s 1.2 standard deviations better than he would have been—a difference bigger than the achievement gap.

The assumption that the advantage from one year to the next does not deteriorate over the summer months is not certain. But even so, the impact of teacher quality is powerful, and virtually everyone in the education community is convinced that the best reform would be an effective teacher in every classroom.

Would it close the achievement gap? Probably not. For an education reform to solve the achievement gap, it must produce bigger gains for black students than for white students. But most education interventions actually exacerbate the gap, and the more effective they are in raising mean achievement, the more they widen the gap. So if every teacher in every American classroom were effective, then all students—black and white—would have an effective teacher and student achievement across the board would rise. Closing the gap means instituting reforms that improve black students’ achievement at a higher rate than white students.

The research also confirms the effectiveness of other teacher reforms. In terms of teachers’ expectations of students, almost all the research shows that if teachers expect more of their students, their students will achieve more. Interventions designed to improve teachers’ expectations have shown modest effects.

Another intervention widely championed is the idea of black teachers teaching black students. Most results show that when black teachers teach black students, black students achieve more than when taught by white teachers. The policy implications are not straightforward. For example, schooling has many different goals—social and emotional ones as well as achievement. Even if the achievement gap would decrease, is it wise to have black students learning only from black teachers?

Instructional Reforms. With a million instructional interventions out there, let’s take one example—Success for All, a highly scripted intervention that can be implemented and replicated well. Rather than striving for excellence per se , Success for All focuses on raising the bottom level of achievement in classrooms. Many studies of this program find good effects—and greater effects, in fact, for black students than for white students. One could hypothesize that the intervention provides the opportunity to learn that black students tend to miss out on.

Another example of a popular instructional reform is reduced class size, but the results are mixed. The best study of class size—the Tennessee STAR study—demonstrated that reducing class size to 15 or below, a fairly major reduction, can have a good-sized effect on achievement in year one. In years two, three, and four, that first effect was maintained, but there was no additional advantage. I can’t think of a more expensive education intervention than this one, and its effect size is disappointing. Moreover, as California’s experience demonstrates, bringing reduced class size to scale can be a perilous task. When that state decided to reduce class size massively, they had to hire new teachers—many of them unqualified—and haul in trailers for classes. For one of the most expensive educational interventions out there, the impact of reducing class size leaves something to be desired.

Research on ability grouping and tracking delivers the counterintuitive news that it is enriched classes that tend to have positive effects on student achievement. Remedial classes, on the other hand, don’t have a negative impact but don’t provide much benefit either.

Following the release of A Nation At Risk in 1983, states increased their requirements for high school graduation and, in one way or another, have been continuing on that path ever since. Predictions were dire. First people foresaw a big decline in high school graduation rates, which didn’t materialize. Then, they were certain that enrollment in remedial courses would increase, and that didn’t materialize when students signed up for more college prep courses. Then, people predicted that teachers would dumb down those college prep courses—which also didn’t materialize: teachers continued to teach college prep courses as they always had.

And the effect on student achievement was huge. In one study, we looked at three contrasting ninth-grade mathematics curricula: Basic Math; Transition Math, which upgraded the curriculum but not to the level of college prep; and College Prep. Controlling for initial differences between students, the value added for student achievement was biggest for College Prep, followed by Transition Math. And then Basic Math was by far worst. Those findings represented a big success story for the idea that students benefit from being given an opportunity to learn at a higher level.

As for promotion and retention policies , the reviews are mixed. Because these policies are administered in so many different ways, evaluations of their effectiveness vary widely. The big hole in this research is that studies only compare kids who are retained versus kids who are promoted—they do not consider the changes to the system over time. Advocates of these policies argue that retaining students will be so painful to the system that it will be forced to improve. We don’t know whether that is happening but we do know that conducting the research to find out will be expensive.

Standards-based Reforms. The standards movement can claim some exciting accomplishments—most notably, putting student achievement on the map. Today, if you go to a school board meeting, if you talk to a superintendent or a principal or a teacher, you’ll hear people talking about improving achievement. Twenty years ago, those conversations had nowhere near the intensity they do now. Also on the plus side—at least for education researchers—is the focus on education research, on connecting research to practice.

But standards-based reform has been with us for ten or 15 years—first at the state level and now in the form of No Child Left Behind—and it does seem that by now, we would be seeing improvements that we’re just not seeing. But remember, too, that anything worth doing can be done poorly, and standards-based reform is no exception to that rule.

Addressing the Gap in Terms of Opportunity

Schools are not the major cause of the achievement gap. Long before kids go to school, the gap is alive and well, and, during the academic year when kids are actually in the classroom, it tends not to increase. Any increases that do occur take place largely outside the context of schooling.

Still, it is the schools we turn to for a solution. But we do well to remember that we are asking schools to solve a problem not of their own making. For schools to solve the achievement gap, we will need much more aggressive interventions—interventions that address the critical issue of opportunities to learn—particularly the opportunities we do (or don’t) provide to our most disadvantaged children.

The most promising reforms are alike in their attention to addressing the pervasive inequalities in opportunities to learn. Consider preschool. Done well, it shows some impressive effects, some lasting effects. But we need to make sure that the kids—all the kids—get this high-quality preschool. This is an opportunity to learn issue.

Consider teacher quality: the research shows that black students have less access to high-quality teachers than white students do and less access to good materials. This is an opportunity to learn issue.

Consider student course-taking patterns. The percentage of students taking college prep high school coursework is going way up for white students, for black students, and for Hispanic students. Over the last 20 years, the gap between black and white students in course-taking has dramatically reduced. This is an opportunity to learn issue—one where we have made real progress.

The achievement gap is unlikely to be totally eliminated by school reform. But that doesn’t get education off the hook. Some education reforms, especially those that provide greater opportunities to learn, do reduce the gap. High-quality preschool, effective teachers in every classroom, a challenging curriculum of enriched classes—all have been shown to have demonstrable effects on students’ academic performance and all have the potential to reduce the achievement gap.

[ back to top ]

Media Inquiries

Penn GSE Communications is here to help reporters connect with the education experts they need.

Kat Stein Executive Director of Penn GSE Communications (215) 898-9642 [email protected]

Center for American Progress

Addressing the Gap Between Education Research and Practice

Report PDF (150 KB)

The Need for State Education Capacity Centers

This proposal aims to connect education leaders and education researchers.

Education, Education, K-12

Media Contact

Mishka espey.

Senior Manager, Media Relations

[email protected]

Sarah Nadeau

Associate Director, Media Relations

Government Affairs

Madeline shepherd.

Director, Federal Affairs

Research alone does not change practice. This is true in every field, be it engineering, education, or law. Studies are not enough to shift the day-to-day practice and habits of professionals; just putting information into someone’s hands does not help them understand how to use that information to improve their work.

Part of the reason is human nature; change is difficult. Another reason is that it takes work to make a study relevant to what’s happening on the ground. Plus, practitioners are often skeptical of experts. Academics, after all, often seem far-removed from the daily work experience.

This issue brief attempts to address the research-practice gap in the education space. First, it describes some challenges in applying research to educational practice. It then outlines research-practice partnerships (RPPs)—mutually beneficial collaborations between research scientists and education leaders that can narrow the gap between research and practice.

Finally, the brief proposes the creation of state-level education capacity centers, which would help leaders in state and local education departments use research to inform practice. Although there is a wealth of education research, most studies do not reflect the context in which district and state education leaders operate. Several state education agencies already have their own research offices, but these offices often do not have the money or personnel to fulfill the state’s many research needs. 1 Especially at the district level, education departments often do not have the capacity to interpret and try to implement the latest research in a timely way. For one thing, education departments struggle to attract and retain research scientists on staff. 2 Second, research offices are often the first to be cut when budgets decline. And because education departments are subject to rapidly evolving local, state, and national political contexts, the priorities that they face are constantly changing, and the majority of time is spent fulfilling accountability reporting requirements, not research on how to improve teaching and learning.

Education capacity centers would address these barriers by facilitating and supporting RPPs between education leaders and external researchers. Nearly every state has at least one premier research institution, but not enough is done to promote collaboration between research systems and school systems.

Education capacity centers would bridge the divide by matching researchers and education leaders; supporting collaborative projects around problems or practice; and engaging practitioners across a given state on the implications of the findings for their districts. The centers could be funded through a mix of public and private funds and administered either in or outside the state education agency. Legislation that establishes the centers could identify a dedicated revenue stream or earmark a reasonable amount of an existing appropriation to support them.

RPPs have proven to be useful in helping leaders improve student outcomes. State education capacity centers would expand these partnerships, as well as direct more government funding beyond the private sector—to instead support RPPs—to serve a larger public good.

Thirty years ago, Chicago Public Schools embarked on a bold experiment. Following a major teacher strike and massive public dissatisfaction, the district dismantled itself and gave decision-making power to community-led local school councils. To accompany this experiment, local and state leaders established the University of Chicago Consortium on School Research to study the impacts of the local school councils and every major reform thereafter. 3

Chicago’s experiment with decentralization did not last long, with decision-making authority shortly restored to the city. 4 But the consortium has withstood the test of time; it continues to provide local leaders in Chicago—and in the rest of the country—with independently produced, timely information on how its range of policies and programs are performing in the city.

Unfortunately, Chicago is unusual in having such strong research capacity—many of today’s school districts do not have robust in-house research and evaluation efforts. More often, school districts struggle to apply lessons from research, largely because their organizations do not have the adequate personnel or time to do so. 5

To be sure, schools want to engage with the latest research to help them improve. But state and local education departments are responsible for every aspect of operating schools, from hiring and supervising staff to deciding how and which subjects students learn. These agencies are largely set up to complete these functions in compliance with state and federal laws, not to respond to the latest research findings.

Yet to an increasing degree, state and local education departments are trying to apply the latest research to improve the quality of education that students receive. Federal lawmakers have incentivized efforts to expand research use by requiring state and local leaders to use evidence when designing interventions to improve their lowest-performing schools and districts. 6 But local education agencies need support behind simple incentives to incorporate research into their work more effectively.

Across the country, several districts have followed Chicago’s example and built relationships with their local research communities to better understand and learn from what is happening in their schools. But geographic distance between education leaders and research institutions, as well as a lack of interested researchers, prevent many communities with struggling schools from taking the same action.

A disconnect between research and practice

There is not a linear pathway from evidence to decision-making. Officials in school districts or state departments of education often struggle to find research relevant to their specific contexts. This issue is not necessarily a concern of the researchers producing the studies, and studies on a particular subject may have contradictory findings. Further, previously held professional experiences or beliefs, organizational priorities, and political demands influence how education leaders interpret this evidence and ultimately make decisions. 7

Furthermore, a lot of attention has focused on “effectiveness” research to evaluate whether a specific practice or program, once implemented, affects an education outcome. 8

Federal policymakers have tried to encourage district- and state-level leaders to use research—largely by trying to improve the quality of research produced, making research more available to state and local leaders, and adopting evidence requirements. 9 The logic behind this approach makes sense, but primarily focusing on research production and dissemination has done little to address the real-world challenges that keep education departments from using research. Evidence does not suggest that dissemination of effectiveness research in and of itself actually changes what practitioners do. 10

In order for state and local leaders to use research-based interventions to improve schools, there need to be policies that reflect how these leaders actually use research to make decisions. Studies have shown that practitioners prefer research that can inform their own improvement efforts, rather than effectiveness research. 11 This type of research can include traditional improvement science, but also descriptive analyses of how national issues affect a specific district or school, as well as the development of student on-track indicators or school climate measures to assess how students and schools are faring. Research focused on improvement is co-constructed by practitioners and researchers, and it is rooted in the challenges of practitioners’ everyday work. Policy should support the creation of this type of research, as well as develop school districts’ and state education departments’ capacity to use findings to implement changes.

The Philadelphia School District’s Office of Research and Evaluation

Philadelphia’s Office of Evaluation, Research, and Accountability houses the Office of Research and Evaluation, which builds partnerships throughout the city’s research community and beyond, leveraging these relationships to improve the district. Philadelphia is home to a range of world-class research institutions such as the University of Pennsylvania, various research consortia, and independent research organizations.

The district’s Office of Evaluation, Research, and Accountability pursues and maintains partnerships with personnel from these organizations and provides information to inform day-to-day decision-making in the district. It supports the district’s central office and school staff. 12

Research-practice partnerships

RPPs can help produce actionable research and help practitioners interpret and apply findings. Cynthia Coburn, William Penuel, and Kimberly Geil, who are established leaders in this field, define RPPs as “long term, mutualistic collaborations between practitioners and researchers that are intentionally organized to investigate problems of practice and solutions for improving district outcomes.” 13

When operationalized, RPPs can take several forms. Some are partnerships between district research leads and outside researchers who study the impacts of district policy while producing independent, generalizable, and publicly available studies. Other RPPs consist of researchers and senior leaders working together to use research to design new interventions and test their effectiveness. RPPs could also help multiple practitioners and researchers form a network in which they develop and test new programs, then share findings among the larger network. 14

Regardless of the specific form that an RPP takes, however, these relationships have several overarching commonalities. In all types of RPPs, researchers and practitioners work together to decide on a research agenda that addresses both practitioners’ needs and researchers’ interests.

Equally important, members on both sides of the relationship are committed to working together for the long term. In an RPP, the emphasis is on working together to build knowledge over time, through an iterative process of implementation and evaluation, rather than just producing one product. 15 Because of these specific features, RPP relationships can better align the effectiveness knowledge that researchers tend to produce with the more applicable, improvement research that practitioners seek out. 16 The research that RPPs generate can be more useful than effectiveness research conducted in other districts because it is conducted under local conditions with the district’s own students and schools.

RPPs have a strong potential to help local and state education departments build capacity and better use research to ultimately improve student outcomes, but several obstacles prevent their widespread adoption. This is primarily because school districts and state departments of education have missions and purposes that are fundamentally different from those of research universities, where many researchers who support RPPs are housed. These researchers may also work in nonprofit research organizations. This results in conflicting organizational structures between these two types of institutions, which make it difficult for members to come together to develop and sustain RPPs.

The UChicago Consortium for School Research

The University of Chicago Consortium for School Research was established in 1990. After Chicago experimented with decentralizing governance of its public schools, the consortium was created to study the impacts of that decision and of subsequent reforms.

Researchers from the University of Chicago, Chicago Public Schools, and other local organizations make up the consortium, and a nonpartisan steering committee with multiagency representation oversees it. 17 Recently, the consortium has evaluated the city’s high-school student assignment system to help policymakers better understand the impact of various criteria involved in the assignment algorithm. 18

In addition, research conducted through the consortium has helped school and district leaders focus their attention on high school students’ ninth-grade coursework, after determining that students’ grades were more predictive of high school success than were test scores. 19 An independent study found that Chicago public school students learned the most when compared with their peers in any other school district in the nation from 2009 through 2014, and stakeholders broadly agree that the consortium contributed to this success. 20

Barriers and obstacles for RPPs

In September 2017, the Center for American Progress convened nearly 30 experts involved in RPPs to assess the field’s present state and identify areas where policy interventions could facilitate the creation and maintenance of these relationships.

Policy experts from all levels of government, RPP leaders, and research leaders from the nation’s largest school districts were in attendance. Aside from the clear reality that in some places, there simply are not enough researchers to partner actively with local and state education agencies for the long term, participants identified the following as the most significant structural challenges that inhibit the use of RPPs: 21

Education agencies vary in their capacity to use research. For a variety of reasons, school districts and state departments of education can have limited capacity to participate in an RPP. It can be challenging to find qualified researchers to work in education agencies, and it has been difficult for school districts to maintain research positions when subject to budget cuts, as research is less of an immediate concern. 22 And even when education departments have research personnel, these personnel often do not have the time to engage in long-term projects and analyses, focused instead on their short-term compliance work.
Education agencies and research institutions have conflicting priorities. Education agencies oversee schools or districts, while research universities are focused on producing research. Although not all researchers who participate in RPPs work at a university, many do. The tenure incentive system from which university-based researchers benefit encourages them to produce generalizable research, while education department leaders need clear answers to immediate, pressing local concerns. 23
It is difficult to secure consistent funding. It is hard for members on both ends of RPPs to find adequate funding to support capacity-building and infrastructure for research. Right now, private foundations are a major funding source, but RPPs need stable funding that can withstand leadership changes on the public sector side of the partnership. 24

Despite these shortcomings, RPPs have a strong potential to bridge the gap between research and practice. Researchers use their expertise to collect, compile, and analyze information, and they support practitioners engaged in the day-to-day work of using the data and research to make decisions. Because these relationships are long-term in nature, researchers can compile a significant amount of data and research over time. This enables them to bring a unique knowledge of institutional history to districts. 25 In this way, RPPs help school districts and state departments of education build their own capacity to use and generate research effectively.

Creating state-level education capacity centers

Because of the challenges outlined above, it can be difficult for RPPs to succeed without considerable support. Specifically, RPPs need an adequate infrastructure, both in terms of physical resources and ample staff time, to coordinate and overcome potential organizational barriers. Right now, many RPPs depend primarily on private philanthropy for support, even though there are limited sources for federal funding. 26 But since these relationships can meet a pressing public need, the state level of government should do more to support RPPs.

At the federal level, the Institute of Education Sciences’ Regional Educational Laboratory (REL) Program partners researchers with practitioners to conduct and use applied research. 27 But the RELs are not able to cover all the education-research needs of states and localities. Moreover, RELs are dependent on federal appropriations for support, and bureaucratic regulations somewhat constrain the work RELs are able to do. A state-level entity designed to bridge institutional divides and foster long-term collaboration between researchers and education department leaders would make RPPs a more realistic, attainable option. State-level education capacity centers would serve as RPP incubators. They would support education departments’ capacity for greater research use by:

Facilitating a process to match researchers with local or state education agency personnel : Geographic distance between research institutions and education agencies can make it difficult to develop and sustain an RPP. Other times, it may be that there are not enough suitable researchers to partner with every education agency in need. A state’s education capacity center would match education department leaders with researchers based on a state needs assessment and the education department’s ability to participate in an RPP effectively.
Soliciting and evaluating joint proposals for long-term collaborative agendas that address local or state priorities : Trust is essential to developing and maintaining an RPP. 28 A state’s education capacity center would oversee an application process that would require all members of the potential RPP to come together to create a joint agenda and collaborative projects. The center could also assist education leaders in preparing to work with researchers.
Awarding initial funding to develop infrastructure: It is easy for members of an RPP to obtain funding for specific projects but fairly difficult to secure funding for the startup costs necessary to support collaborative work. 29 A state’s education capacity center would provide the funding necessary to build the infrastructure to support RPPs and would cover the cost of usable databases and other physical equipment, as well as coordinating staff members’ time and finalizing partnership agreements or memoranda of understanding.
Providing technical assistance to structure the partnership and identify long-term funding sources : After developing infrastructure for an RPP, the researchers and practitioners would need funding to support specific projects. A state’s education capacity center would help connect members of the RPP with sustainable, long-term funding sources, as well as provide any additional technical assistance necessary to sustain the relationship.
Fostering a statewide network to use the findings to improve local systems : A state’s education capacity center would bring together those participating in an RPP from across the state to create an improvement community network. Members of this network would meet periodically to share findings from their collaborative projects, allowing practitioners who are not in an RPP supported by an education capacity center to benefit from this work as well.

Education capacity centers would be established through state legislation and could either be part of the state education agency or operate independently, depending on a given state’s needs or context. They could also be funded by private groups and exist as a nonprofit. In states that opt for a semipublic agency, a board with representation from state education agencies, governor’s offices, and legislatures—as well as representation from the state teachers’ union, higher education, and district leadership—communities could govern the center. In states that already have a strong capacity for research use in the state education agency, the education capacity center would be a natural extension in that it would build the capacity of districts to use research better.

Last year, Maryland state Sen. Bill Ferguson (D) introduced S.B. 0908, which would create the Maryland Education Development Collaborative (EdCo). EdCo would be a quasi-public state-level agency that fosters innovative school designs and practices by funding and supporting RPPs in Maryland and by spreading findings from their research across the state. EdCo would make recommendations to the state Board of Education, General Assembly, and local school districts, as well as support studies consistent with pressing priorities. EdCo would be the first entity of its kind in the nation. 30

Education capacity centers that cultivate RPPs at the state level would help many more school districts leverage research to improve educational outcomes for students. RPPs provide a promising model, but the institutional obstacles—combined with geographic realities and a limited number of researchers—puts these relationships out of reach for too many schools and districts.

Research is an essential part of the school improvement process. The nation’s public schools have a long way to go to provide every child with the opportunity to obtain a great education, and an education capacity center would help more schools and districts take necessary steps forward.

Ulrich Boser is a senior fellow at the Center for American Progress. Abel McDaniels is a research associate for K-12 Education at the Center.

Margaret E. Goertz, Carol Barnes, and Diane Massell, “How State Education Agencies Acquire and Use Research in School Improvement Strategies” (Philadelphia: Consortium for Policy Research in Education, 2013), available at http://www.cpre.org/sea-brief .
Ruth N. López Turley and Carla Stevens, “Lessons From a School District-University Research Partnership: The Houston Education Research Consortium,” Educational Evaluation and Policy Analysis 37 (1) (2015), available at http://journals.sagepub.com/doi/abs/10.3102/0162373715576074 .
Denisa R. Superville, “Chicago’s Local Schools Council ‘Experiment’ Endures 25 Years of Change,” Education Week, October 7, 2014, available at https://www.edweek.org/ew/articles/2014/10/08/07chicagocouncil.h34.html ; University of Chicago Consortium on School Research, “About Us,” available at https://consortium.uchicago.edu/about (last accessed April 2018).
Superville, “Chicago’s Local Schools Council ‘Experiment’ Endures 25 Years of Change.”
Meredith I. Honig, Nitya Venkateswaran, and Patricia McNeil, “Research Use as Learning: The Case of Fundamental Change in School District Central Offices,” American Educational Research Journal 54 (5) (2017).
Mark Dynarski, “Using research to improve education under the Every Student Succeeds Act” (Washington: Brookings Institution, 2015), available at https://www.brookings.edu/research/using-research-to-improve-education-under-the-every-student-succeeds-act/ .
Cynthia Coburn, Mary Kay Stein, and Meredith I. Honig, “What’s the evidence on districts’ use of evidence?” In John D. Bransford and Louis Gomez eds., The Role of Research in Educational Improvement (Cambridge, MA: Harvard Education Press, 2009), available at https://www.researchgate.net/publication/242644678_What’s_the_evidence_on_districts’_use_of_evidence .
Dynarski, “Using research to improve education under the Every Student Succeeds Act.”
Vivian Tseng, Lauren H. Supplee, and John Q. Easton, “Research-Practice Partnerships: Building Two-Way Streets of Engagement,” Society for Research in Child Development 30 (4) (2017), available at https://www.srcd.org/sites/default/files/documents/spr_30_4.pdf .
Dynarski, “Using research to improve education under the Every Student Succeeds Act”; Steve Fleischman, “Research Matters/Moving to Evidence-Based Professional Practice,” Educational Leadership 63 (6) (2006), available at http://www.ascd.org/publications/educational_leadership/mar06/vol63/num06/Moving_to_Evidence-Based_Professional_Practice.aspx .
Goertz, Barnes, and Massell, “How State Education Agencies Acquire and Use Research in School Improvement Strategies.”
Philadelphia School District Office of Research and Evaluation, “Welcome to the Office of Research and Evaluation at the School District of Philadelphia!”, available at https://www.philasd.org/research/ (last accessed April 2018).
Cynthia E. Coburn, William R. Penuel, and Kimberly E. Geil, “Research-Practice Partnerships: A Strategy for Leveraging Research for Educational Improvement in School Districts” (New York: William T. Grant Foundation, 2013), available at http://wtgrantfoundation.org/library/uploads/2015/10/Research-Practice-Partnerships-at-the-District-Level.pdf .
Tseng, Supplee, and Easton, “Research-Practice Partnerships.”
University of Chicago Consortium on School Research, “About Us.”
Lisa Barrow, Lauren Sartain, and Marisa de la Torre, “Selective Enrollment High Schools in Chicago: Admission and Impacts” (Chicago: University of Chicago Consortium on School Research, 2018), available at https://consortium.uchicago.edu/publications/selective-enrollment-high-schools-chicago-admission-and-impacts .
John Q. Easton, Esperanza Johnson, and Lauren Sartain, “The Predictive Power of Ninth-Grade GPA” (Chicago: University of Chicago Consortium on School Research, 2017), available at https://consortium.uchicago.edu/publications/predictive-power-ninth-grade-gpa .
Sean F. Reardon and Rebecca Hinze-Pifer, “Test Score Growth Among Chicago Public School Students, 2009-2014” (Stanford, CA: Center for Education Policy Analysis, 2017), available at https://cepa.stanford.edu/content/test-score-growth-among-chicago-public-school-students-2009-2014 ; Maureen Kelleher, “Progress and Promise: Chicago’s Nation-Leading Educational Gains” (Chicago: The Joyce Foundation and the Spencer Foundation, 2018), available at http://www.joycefdn.org/assets/images/1063JYC-CPS-Forum-Report-WEB-3.pdf .
López Turley and Stevens, “Lessons From a School District-University Research Partnership.”
Information from a private convening with school district and state education leaders, researchers, and education policy experts, Center for American Progress, September 29, 2017.
Coburn, Penuel, and Geil, “Research-Practice Partnerships.”
Regional Educational Laboratory Program, Institute of Education Sciences, “Putting Research into Action,” available at https://ies.ed.gov/ncee/edlabs/ (last accessed May 2018).
Coburn, Penuel, and Geil, “Research-Practice Partnerships”; López Turley and Stevens, “Lessons From a School District-University Research Partnership”; Tseng, Supplee, and Easton, “Research-Practice Partnerships.”
López Turley and Stevens, “Lessons Frin a School District-University Research Partnership.”
Maryland General Assembly Department of Legislative Services, “Senate Bill 908 Budget and Taxation, Fiscal and Policy Note: Enrolled – Revised,” available at http://mgaleg.maryland.gov/2017RS/fnotes/bil_0008/sb0908.pdf (last accessed March 2018).

The positions of American Progress, and our policy experts, are independent, and the findings and conclusions presented are those of American Progress alone. A full list of supporters is available here . American Progress would like to acknowledge the many generous supporters who make our work possible.

Ulrich Boser

Former Senior Fellow

Abel McDaniels

Research Associate

AI and the next digital divide in education

Subscribe to the center for universal education bulletin, michael trucano michael trucano visiting fellow - global economy and development , center for universal education.

July 10, 2023

The evolution of the “digital divide”:

The first digital divide : The rich have technology, while the poor do not.

The second digital divide : The rich have technology and the skills to use it effectively, while the poor have technology but lack skills to use it effectively.

The third digital divide? : The rich have access to both technology and people to help them use it, while the poor have access to technology only.

The theme of the most recent Education World Forum (EWF), the world’s largest annual gathering of education ministers, was “ new beginnings .” The program featured perspectives from education leaders from all over the world on a variety of topics, many of them evergreen: access to education; educational quality; equity; jobs; skills; the role of teachers; gender; and sustainability. Post-pandemic, more attention was paid to issues of building resilience in education systems than it had been in past years. Reflecting larger societal trends, discussions of the role of education vis-a-vis climate change were heard more often, and at a higher volume. However, one new topic did serve as a sort of thematic connective tissue across all three days of discussions, infusing doses of concern, confusion, worry, and excitement into considerations of whatever was on the formal agenda for ministerial deliberation: the potential role and impact of artificial intelligence (AI) in education .

I sat in on one well-attended and lively discussion session in which a participant recounted a recent assembly at a university in a lower-middle income country in Asia where a student asked a question about the use of ChatGPT—the chatbot that ignited the current explosion of excitement about AI use in education when it was released late last year. The head of the university quickly interrupted, noting that such questions were largely theoretical at the institution as the tool was not yet used in the country in any real way. The speaker then asked the few hundred students in the audience if they had ever used ChatGPT—100 percent of them raised their hands. (Eighty percent of the students kept their hands up when asked a follow-up question: “And how many of you have used it in the last 24 hours?”) Responses in the EWF event hall were a mix of looks of concern and knowing chuckles.

In response, one minister expressed excitement about what new AI tools could do for students in his country, especially those not enrolled in school, those in classrooms where the student-to-teacher ratio often exceeds 60:1, or those in schools where teachers are inadequately trained or poorly supported. We don’t have enough qualified teachers for all of our students and aren’t likely to in the near future , he said . AI technology can bridge this gap.

The promise of AI and personalized learning

The promise of a personalized digital tutor or teacher, an always-on “ teaching machine ” that never sleeps and is responsive to the cognitive needs of an individual learner, has been a consistent theme across the history of educational computing. Influential academic papers and even science fiction books have inspired generations of educational software and hardware developers to create tools and devices to enable more personalized learning. While to date the impact of such efforts has been mixed , at best, and related rhetoric has often outstripped observable reality, progress is being made—and quickly. Tools like Khanmigo from Khan Academy, which is built on the AI technology that powers ChatGPT, offer tantalizing glimpses of what may soon be possible and accessible for millions of learners around the world.

In a world experiencing a global learning crisis , where as many as 70 percent of 10-year-olds in low- and middle-income economies can’t read and understand a basic text, over 244 million children and youth are out of school , and there is a projected global teacher shortage of almost 70 million teachers by 2030, a new wave of AI-enabled educational technology innovations can’t come too soon.

Digital divides in education

The existence of a “ digital divide ” in education—the idea that some children, families, teachers, and schools have access to information and communications technologies to support learning and others do not—has been observed and lamented for over a generation. As access to computing devices has improved, a “ second digital divide ” has emerged which, according to the OECD , “separates those with the competencies and skills to benefit from computer use from those without.” Increasingly recognizing this, education systems around the world—rich and poor alike—consider difficult trade-offs related to investing in computing infrastructure and the cultivation of teachers’, learners’, and administrators’ skills to make productive use of this infrastructure. This often happens in fiscal environments where resources are constrained , related know-how is scarce, and the challenges affecting the sector remain abundant. At the same time, it’s hard to go a day without reading a headline speculating about the potential for new advances in artificial intelligence to transform education . Might access to AI represent a new (third?) digital divide in education?

Might access to AI represent a new (third?) digital divide in education?

To be clear: The existing digital divides in education, gaps traditionally measured in the number of computing devices available to support teaching and learning and the availability, speed, and reliability of internet connectivity, aren’t going away any time soon. While access to educational computing tools is near ubiquitous in countries like South Korea and Estonia , stubborn gaps remain across education systems even in the “advanced” economies of the OECD in terms of both access and skills. The reality in less developed countries in Africa , Asia , and Latin America is worse. And even where schools are well-resourced and connected to the internet, access at home is another matter , as the recent global experiment in emergency remote learning made clear .

While progress in this area isn’t being made fast enough for many learners—especially those in marginalized communities and in the poorest countries—the playbook for progress is largely known. Even where components of a solution may involve getting internet access from satellites orbiting the earth, this isn’t rocket science: Related progress is a function of a combination of money, planning, and political will. Whether measured in days or decades, the related trend line over time is positive.

Let’s posit that the connectivity challenge in education can be solved, and will be, and that students will (eventually) have access to their own devices for learning, turbocharged in various ways by AI. What then? Let’s further hypothesize that students will possess the skills and competencies that will enable them to take full and productive use of the technology available to them. Might this mean the end of the “digital divide”?

Looking forward

Closing the digital divide in education has traditionally been about eliminating a gap between the rich and the poor, where, to oversimplify, rich kids have access to lots of devices and fast, reliable connectivity, and poor kids do not.

Is it possible to imagine a future in which a new digital divide emerges: where the rich have access to technology, increasingly powered by artificial intelligence, and to teachers to help them use this technology as part of their learning, while poor kids just have access to the technology?

From the perspective of 2023, when limited access to reliable connectivity and sufficiently powerful computing devices for learning remains the norm in so many schools and communities around the world, this may perhaps sound far-fetched.

That said, some experiences during the COVID-19 pandemic might offer a potential glimpse of things to come. Even in countries where access to devices and the internet at home was widespread and (relatively) equitable, there were still noticeable gaps in achievement between rich kids and poor kids in many places engaged in remote learning. Many plausible reasons have been advanced to explain gaps that occurred during the pandemic related to lack of access to digital learning opportunities and the quality of those opportunities. In addition, an emerging body of research from around the world during the pandemic (in places as diverse as Ghana , Indonesia , Poland , Saudi Arabia , and the United States ) explores how, even when the use of technology played a dominant role in education, the involvement of parents and tutors in the learning process—in other words, people —was often consequential.

It’s been observed that, “The mediocre teacher tells. The good teacher explains. The superior teacher demonstrates. The great teacher inspires.” Proponents of increased use of AI education claim that various flavors of AI will soon be able to perform the first three duties. But what about the fourth? Education is, after all, a fundamentally human endeavor.

What if, in the future, access to technology is something available to all, and not only the privileged, while access to people (engaged parents, private tutors, trained teachers) is limited?

What might this mean—and what might we do about it?

Regions & Countries

Publications
Our Methods
Short Reads
Tools & Resources

Read Our Research On:

The State of the American Middle Class

Who is in it and key trends from 1970 to 2023

Table of contents.

Acknowledgments

This report examines key changes in the economic status of the American middle class from 1970 to 2023 and its demographic attributes in 2022. The historical analysis is based on U.S. Census Bureau data from the Annual Social and Economic Supplements (ASEC) of the Current Population Survey (CPS). The demographic analysis is based on data from the American Community Survey (ACS). The data is sourced from IPUMS CPS and IPUMS USA , respectively.

The CPS, a survey of about 60,000 households, is the U.S. government’s official source for monthly estimates of unemployment . The CPS ASEC, conducted in March each year, is the official source of U.S. government estimates of income and poverty . Our analysis of CPS data starts with the 1971 CPS ASEC, which records the incomes of households in 1970. It is also the first year for which data on race and ethnicity is available. The latest available CPS ASEC file is for 2023, which reports on household incomes in 2022.

The public-use version of the ACS is a 1% sample of the U.S. population, or more than 3 million people. This allows for a detailed study of the demographic characteristics of the middle class, including its status in U.S. metropolitan areas. But ACS data is available only from 2005 onward and is less suitable for long-term historical analyses. The latest available ACS data is for 2022.

Middle-income households are defined as those with an income that is two-thirds to double that of the U.S. median household income, after incomes have been adjusted for household size. Lower-income households have incomes less than two-thirds of the median, and upper-income households have incomes that are more than double the median. When using American Community Survey (ACS) data, incomes are also adjusted for cost of living in the areas in which households are located.

Estimates of household income are scaled to reflect a household size of three and expressed in 2023 dollars. In the Current Population Survey (CPS), household income refers to the calendar year prior to the survey year. Thus, the income data in the report refers to the 1970-2022 period, and the share of Americans in each income tier from the CPS refers to the 1971-2023 period.

The demographic attributes of Americans living in lower-, middle- or upper-income tiers are derived from ACS data. Except as noted, estimates pertain to the U.S. household population, excluding people living in group quarters.

The terms middle class and middle income are used interchangeably in this report.

White, Black, Asian, American Indian or Alaska Native, and Native Hawaiian or Pacific Islander include people who identified with a single major racial group and who are not Hispanic. Multiracial includes people who identified with more than one major racial group and are not Hispanic. Hispanics are of any race.

U.S. born refers to individuals who are U.S. citizens at birth, including people born in the 50 U.S. states, the District of Columbia, Puerto Rico or other U.S. territories, as well as those born elsewhere to at least one parent who is a U.S. citizen. The terms foreign born and immigrant are used interchangeably in this report. They refer to people who are not U.S. citizens at birth.

Occupations describe the broad kinds of work people do on their job. For example, health care occupations include doctors, nurses, pharmacists and others who are directly engaged in the provision of health care. Industries describe the broad type of products companies produce. Each industry encompasses a variety of occupations. For example, the health care and social assistance industry provides services that are produced by a combination of doctors, managers, technology and administrative staff, food preparation workers, and workers in other occupations.

The share of Americans who are in the middle class is smaller than it used to be. In 1971, 61% of Americans lived in middle-class households. By 2023, the share had fallen to 51%, according to a new Pew Research Center analysis of government data.

A bar chart showing that Share of Americans in the middle class has fallen since 1971

As a result, Americans are more apart than before financially. From 1971 to 2023, the share of Americans who live in lower-income households increased from 27% to 30%, and the share in upper-income households increased from 11% to 19%.

Notably, the increase in the share who are upper income was greater than the increase in the share who are lower income. In that sense, these changes are also a sign of economic progress overall.

But the middle class has fallen behind on two key counts. The growth in income for the middle class since 1970 has not kept pace with the growth in income for the upper-income tier. And the share of total U.S. household income held by the middle class has plunged.

Moreover, many groups still lag in their presence in the middle- and upper-income tiers. For instance, American Indians or Alaska Natives, Black and Hispanic Americans, and people who are not married are more likely than average to be in the lower-income tier. Several metro areas in the U.S. Southwest also have high shares of residents who are in the lower-income tier, after adjusting for differences in cost of living across areas.

Change in income
Share of total U.S. household income
Race and ethnicity
Marital status
Veteran status
Place of birth
Employment status
Metropolitan area of residence

Our report focuses on the current state of the American middle class. First, we examine changes in the financial well-being of the middle class and other income tiers since 1970. This is based on data from the Annual Social and Economic Supplements (ASEC) of the Current Population Survey (CPS), conducted from 1971 to 2023.

Then, we report on the attributes of people who were more or less likely to be middle class in 2022. Our focus is on their race and ethnicity , age , gender, marital and veteran status , place of birth , ancestry , education , occupation , industry , and metropolitan area of residence . These estimates are derived from American Community Survey (ACS) data and differ slightly from the CPS-based estimates. In part, that is because incomes can be adjusted for the local area cost of living only with the ACS data. (Refer to the methodology for details on these two data sources.)

This analysis and an accompanying report on the Asian American middle class are part of a series on the status of America’s racial and ethnic groups in the U.S. middle class and other income tiers. Forthcoming analyses will focus on White, Black, Hispanic, American Indian or Alaska Native, Native Hawaiian or Pacific Islander and multiracial Americans, including subgroups within these populations. These reports are, in part, updates of previous work by the Center . But they offer much greater detail on the demographic attributes of the American middle class.

Following are some key facts about the state of the American middle class:

In our analysis, “middle-income” Americans are those living in households with an annual income that is two-thirds to double the national median household income. The income it takes to be middle income varies by household size, with smaller households requiring less to support the same lifestyle as larger households. It also varies by the local cost of living, with households in a more expensive area, such as Honolulu, needing a higher income than those in a less expensive area, such as Wichita, Kansas.

We don’t always know the area in which a household is located. In our two data sources – the Current Population Survey, Annual Social and Economic Supplement (CPS ASEC) and the American Community Survey (ACS) – only the latter provides that information, specifically the metropolitan area of a household. Thus, we aren’t able to adjust for the local cost of living when using the CPS to track changes in the status of the middle class over time. But we do adjust for the metropolitan area cost of living when using the ACS to determine the demographic attributes of the middle class in 2022.

In the 2023 CPS ASEC data , which reports income for 2022, middle-income households with three people have incomes ranging from about $61,000 to $183,000 annually. “Lower-income” households have incomes less than $61,000, and “upper-income” households have incomes greater than $183,000.

In the 2022 ACS data , middle-income households with three people have incomes ranging from about $62,000 to $187,000 annually, with incomes also adjusted for the local area cost of living. (Incomes are expressed in 2023 dollars.)

The boundaries of the income tiers also vary across years as the national median income changes.

The terms “middle income” and “middle class” are used interchangeably in this report for the sake of exposition. But being middle class can refer to more than just income , be it education level, type of profession, economic security, home ownership or social and political values. Class also could simply be a matter of self-identification .

Households in all income tiers had much higher incomes in 2022 than in 1970, after adjusting for inflation. But the gains for middle- and lower-income households were less than the gains for upper-income households .

A bar chart showing that Incomes of upper-income U.S. households increased the most of any income tier from 1970 to 2022

The median income of middle-class households increased from about $66,400 in 1970 to $106,100 in 2022, or 60%. Over this period, the median income of upper-income households increased 78%, from about $144,100 to $256,900. (Incomes are scaled to a three-person household and expressed in 2023 dollars.)

The median income of lower-income households grew more slowly than that of other households, increasing from about $22,800 in 1970 to $35,300 in 2022, or 55%.

Consequently, there is now a larger gap between the incomes of upper-income households and other households. In 2022, the median income of upper-income households was 7.3 times that of lower-income households, up from 6.3 in 1970. It was 2.4 times the median income of middle-income households in 2022, up from 2.2 in 1970.

The share of total U.S. household income held by the middle class has fallen almost without fail in each decade since 1970 . In that year, middle-income households accounted for 62% of the aggregate income of all U.S. households, about the same as the share of people who lived in middle-class households.

A line chart showing that Share of total U.S. household income held by the middle class has plunged since 1970

By 2022, the middle-class share in overall household income had fallen to 43%, less than the share of the population in middle-class households (51%). Not only do a smaller share of people live in the middle class today, the incomes of middle-class households have also not risen as quickly as the incomes of upper-income households.

Over the same period, the share of total U.S. household income held by upper-income households increased from 29% in 1970 to 48% in 2022. In part, this is because of the increase in the share of people who are in the upper-income tier.

The share of overall income held by lower-income households edged down from 10% in 1970 to 8% in 2022. This happened even though the share of people living in lower-income households increased over this period.

The share of people in the U.S. middle class varied from 46% to 55% across racial and ethnic groups in 2022. Black and Hispanic Americans, Native Hawaiians or Pacific Islanders, and American Indians or Alaska Natives were more likely than others to be in lower-income households .

A bar chart showing Black, Hispanic, Native Hawaiian/Pacific Islander and American Indian/Alaska Native people are more likely than others to live in lower-income U.S. households

In 2022, 39% to 47% of Americans in these four groups lived in lower-income households. In contrast, only 24% of White and Asian Americans and 31% of multiracial Americans were in the lower-income tier.

At the other end of the economic spectrum, 27% of Asian and 21% of White Americans lived in upper-income households in 2022, compared with about 10% or less of Black and Hispanic Americans, Native Hawaiians or Pacific Islanders, and American Indians or Alaska Natives.

Not surprisingly, lower-income status is correlated with the likelihood of living in poverty. According to the Census Bureau , the poverty rate among Black (17.1%) and Hispanic (16.9%) Americans and American Indians or Alaska Natives (25%) was greater than the rate among White and Asian Americans (8.6% for each). (The Census Bureau did not report the poverty rate for Native Hawaiians or Pacific Islanders.)

A bar chart showing Nearly 4 in 10 U.S. children lived in lower-income households in 2022, about half in the middle class

Children and adults 65 and older were more likely to live in lower-income households in 2022. Adults in the peak of their working years – ages 30 to 64 – were more likely to be upper income. In 2022, 38% of children (including teens) and 35% of adults 65 and older were lower income, compared with 26% of adults ages 30 to 44 and 23% of adults 45 to 64.

The share of people living in upper-income households ranged from 13% among children and young adults (up to age 29) to 24% among those 45 to 64. In each age group, about half or a little more were middle class in 2022.

Men were slightly more likely than women to live in middle-income households in 2022 , 53% vs. 51%. Their share in upper-income households (18%) was also somewhat greater than the share of women (16%) in upper-income households.

A bar chart showing that Men, veterans and married Americans were more likely than their counterparts to live in middle- or upper-income households in 2022

Marriage appears to boost the economic status of Americans. Among those who were married in 2022, eight-in-ten lived either in middle-income households (56%) or upper-income households (24%). In contrast, only about six-in-ten of those who were separated, divorced, widowed or never married were either middle class or upper income, while 37% lived in lower-income households.

Veterans were more likely than nonveterans to be middle income in 2022, 57% vs. 53%. Conversely, a higher share of nonveterans (29%) than veterans (24%) lived in lower-income households.

A bar chart showing that Immigrants were more likely than the U.S. born to be lower income in 2022; people born in Asia, Europe or Oceania were most likely to be upper income

Immigrants – about 14% of the U.S. population in 2022 – were less likely than the U.S. born to be in the middle class and more likely to live in lower-income households. In 2022, more than a third of immigrants (36%) lived in lower-income households, compared with 29% of the U.S. born. Immigrants also trailed the U.S. born in the shares who were in the middle class, 48% vs. 53%.

There are large gaps in the economic status of American residents by their region of birth. Among people born in Asia, Europe or Oceania, 25% lived in upper-income households in 2022. People from these regions represented 7% of the U.S. population.

By comparison, only 14% of people born in Africa or South America and 6% of those born in Central America and the Caribbean were in the upper-income tier in 2022. Together they accounted for 8% of the U.S. population.

The likelihood of being in the middle class or the upper-income tier varies considerably with the ancestry of Americans. In 2022, Americans reporting South Asian ancestry were about as likely to be upper income (38%) as they were to be middle income (42%). Only 20% of Americans of South Asian origin lived in lower-income households. South Asians accounted for about 2% of the U.S. population of known origin groups in 2022.

A bar chart showing that Americans of South Asian origin are the most likely to be upper income; Hispanic origins are the least likely

At least with respect to the share who were lower income, this was about matched by those with Soviet, Eastern European, other Asian or Western European origins. These groups represented the majority (54%) of the population of Americans whose ancestry was known in 2022.

On the other hand, only 7% of Americans with Central and South American or other Hispanic ancestry were in the upper-income tier, and 44% were lower income. The economic statuses of Americans with Caribbean, sub-Saharan African or North American ancestry were not very different from this.

Education matters for moving into the middle class and beyond, and so do jobs. Among Americans ages 25 and older in 2022, 52% of those with a bachelor’s degree or higher level of education lived in middle-class households and another 35% lived in upper-income households.

A bar chart showing that The share of Americans in the middle- or upper-income tier rises sharply with education and employment

In sharp contrast, 42% of Americans who did not graduate from high school were in the middle class, and only 5% were in the upper-income tier. Further, only 12% of college graduates were lower income, compared with 54% of those who did not complete high school.

Not surprisingly, having a job is strongly linked to movement from the lower-income tier to the middle- and upper-income tiers. Among employed American workers ages 16 and older, 58% were in the middle-income tier in 2022 and 23% were in the upper-income tier. Only 19% of employed workers were lower income, compared with 49% of unemployed Americans.

A bar chart showing that More than a third of U.S. workers in technology, management, and business and finance occupations were in the upper-income tier in 2022

In some occupations, about nine-in-ten U.S. workers are either in the middle class or in the upper-income tier, but in some other occupations almost four-in-ten workers are lower income. More than a third (36% to 39%) of workers in computer, science and engineering, management, and business and finance occupations lived in upper-income households in 2022. About half or more were in the middle class.

But many workers – about one-third or more – in construction, transportation, food preparation and serving, and personal care and other services were in the lower-income tier in 2022.

About six-in-ten workers or more in education; protective and building maintenance services; office and administrative support; the armed forces; and maintenance, repair and production were in the middle class.

A bar chart showing that About a third of U.S. workers in the information, financial and professional services sectors were in the upper-income tier in 2022

Depending on the industrial sector, anywhere from half to two-thirds of U.S. workers were in the middle class, and the share who are upper income or lower income varied greatly.

About a third of workers in the finance, insurance and real estate, information, and professional services sectors were in the upper-income tier in 2022. Nearly nine-in-ten workers (87%) in public administration – largely filling legislative functions and providing federal, state or local government services – were either in the middle class or the upper-income tier.

But nearly four-in-ten workers (38%) in accommodation and food services were lower income in 2022, along with three-in-ten workers in the retail trade and other services sectors.

The share of Americans who are in the middle class or in the upper- or lower-income tier differs across U.S. metropolitan areas. But a pattern emerges when it comes to which metro areas have the highest shares of people living in lower-, middle- or upper-income households. (We first adjust household incomes for differences in the cost of living across areas.)

A bar chart showing that The 10 U.S. metropolitan areas with the largest shares of residents in the middle class in 2022

The 10 metropolitan areas with the greatest shares of middle-income residents are small to midsize in population and are located mostly in the northern half of the U.S. About six-in-ten residents in these metro areas were in the middle class.

Several of these areas are in the so-called Rust Belt , namely, Wausau and Oshkosh-Neenah, both in Wisconsin; Grand Rapids-Wyoming, Michigan; and Lancaster, Pennsylvania. Two others – Dover and Olympia-Tumwater – include state capitals (Delaware and Washington, respectively).

In four of these areas – Bismarck, North Dakota, Ogden-Clearfield, Utah, Lancaster and Wausau – the share of residents in the upper-income tier ranged from 18% to 20%, about on par with the share nationally.

A bar chart showing that The 10 U.S. metropolitan areas with the largest shares of residents in the upper-income tier in 2022

The 10 U.S. metropolitan areas with the highest shares of residents in the upper-income tier are mostly large, coastal communities. Topping the list is San Jose-Sunnyvale-Santa Clara, California, a technology-driven economy, in which 40% of the population lived in upper-income households in 2022. Other tech-focused areas on this list include San Francisco-Oakland-Hayward; Seattle-Tacoma-Bellevue; and Raleigh, North Carolina.

Bridgeport-Stamford-Norwalk, Connecticut, is a financial hub. Several areas, including Washington, D.C.-Arlington-Alexandria and Boston-Cambridge-Newton, are home to major universities, leading research facilities and the government sector.

Notably, many of these metro areas also have sizable lower-income populations. For instance, about a quarter of the populations in Bridgeport-Stamford-Norwalk; Trenton, New Jersey; Boston-Cambridge-Newton; and Santa Cruz-Watsonville, California, were in the lower-income tier in 2022.

A bar chart showing that The 10 U.S. metropolitan areas with the largest shares of residents in the lower-income tier in 2022

Most of the 10 U.S. metropolitan areas with the highest shares of residents in the lower-income tier are in the Southwest, either on the southern border of Texas or in California’s Central Valley. The shares of people living in lower-income residents were largely similar across these areas, ranging from about 45% to 50%.

About 40% to 50% of residents in these metro areas were in the middle class, and only about one-in-ten or fewer lived in upper-income households.

Compared with the nation overall, the lower-income metro areas in Texas and California have disproportionately large Hispanic populations. The two metro areas in Louisiana – Monroe and Shreveport-Bossier City – have disproportionately large Black populations.

Note: For details on how this analysis was conducted, refer to the methodology .

Sign up for our weekly newsletter

Fresh data delivery Saturday mornings

Sign up for The Briefing

Weekly updates on the world of news & information

Income, Wealth & Poverty
Middle Class

The State of the Asian American Middle Class

Black and hispanic americans, those with less education are more likely to fall out of the middle class each year, how the american middle class has changed in the past five decades, covid-19 pandemic pinches finances of america’s lower- and middle-income families, are you in the global middle class find out with our income calculator, most popular, report materials.

Methodology

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 | Media Inquiries

Research Topics

Email Newsletters

ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

IMAGES

Education Gap Analysis
The Education-Research Gap in Universities.
Education Gap Analysis
Education Gap Analysis
Education Gap Analysis
Education Gap Analysis

VIDEO

Understanding Educational Opportunity Gaps
UNVEILING THE EDUCATION GAP:part 4. #Godfirst #education #success
Bridging the Education Gap #shortsviral #shorts
JHU School of Education: Closing the Education Gap Part 3
Education Gap Nomination Process
School building gap: Students face disparities in learning environments

COMMENTS

A decade of research on education inequality in America
An analysis of achievement gaps in every school in America shows that poverty is the biggest hurdle. Rich schools get richer: School spending analysis finds widening gap between top 1% and the rest of us. This story about education inequality in America written by Jill Barshay and produced by The Hechinger Report, a nonprofit, independent news ...
Education Gap Grows Between Rich and Poor, Studies Say
Education was historically considered a great equalizer in American society, capable of lifting less advantaged children and improving their chances for success as adults. But a body of recently published scholarship suggests that the achievement gap between rich and poor children is widening, a development that threatens to dilute education's leveling effects.
This data shows the racial gap in access to education in the US
Racial achievement gaps in the United States are narrowing, a Stanford University data project shows. But progress has been slow and unsteady - and gaps are still large across much of the country. COVID-19 could widen existing inequalities in education. The World Economic Forum will be exploring the issues around growing income inequality as ...
Education inequalities at the school starting gate: Gaps, trends, and
What this study finds: Extensive research has conclusively demonstrated that children's social class is one of the most significant predictors—if not the single most significant predictor—of their educational success.Moreover, it is increasingly apparent that performance gaps by social class take root in the earliest years of children's lives and fail to narrow in the years that follow.
How COVID taught America about inequity in education
"Unequal" is a multipart series highlighting the work of Harvard faculty, staff, students, alumni, and researchers on issues of race and inequality across the U.S. This part looks at how the pandemic called attention to issues surrounding the racial achievement gap in America. The pandemic has disrupted education nationwide, turning a spotlight on existing racial and economic disparities ...
The costs of inequality: Education's the one key that rules them all
Education may be the key to solving broader American inequality, but we have to solve educational inequality first. Ronald Ferguson, director of the Achievement Gap Initiative at Harvard University, says there is progress being made, there are encouraging examples to emulate, that an early start is critical, and that a lot of hard work lies ahead.
COVID-19 and the widening learning gap
The Utah Education Policy Center's research brief on chronic absenteeism calculates the overall correlation between one year of chronic absence from eighth to 12th grade and dropping out of school is 0.134. ... New evidence on school segregation and racial academic achievement gaps, Stanford Center for Education Policy Analysis working paper ...
How uneven educational outcomes begin, and persist, in the US
How uneven educational outcomes begin, and persist, in the US. Long before graduation, factors including early education, household income gaps, and disciplinary actions affect students' abilities to access resources and succeed in school. These elements impact racial and ethnic groups differently and contribute to these unequal educational ...
PDF Report on the Condition of Education 2023
May 2023. On behalf of the National Center for Education Statistics (NCES), I am pleased to present the 2023 edition of the Condition of Education. The Condition is an annual report mandated by the U.S. Congress that summarizes the latest data on education in the United States, including international comparisons.
Frontiers
Educational strategies to reduce the achievement gap: a systematic review. Carmo Cabral-Gouveia Isabel Menezes Tiago Neves *. CIIE—Center for Research and Intervention in Education, Faculty of Psychology and Education Sciences, University of Porto, Porto, Portugal. Despite continuous efforts, the educational achievement gap is still, in most ...
The achievement gap in education: Racial segregation versus segregation
According to a new study by Reardon, Weathers, Fahle, Jang, and Kalogrides on segregation's effects on racial achievement gaps, segregation reached its peak in 1968, declined through about 1980 ...
Full article: Socio-economic inequality and education
The thematic issue also highlights the continuing need to focus on inequality in education, and particularly on the central role of socio-economic factors as one of the most fundamental drivers of unequal educational outcomes (Strand, 2022 ). This issue demonstrates that, 50 years since its inception, the Oxford Review of Education continues to ...
Racial and Ethnic Achievement Gaps
Over the past 40 years, white-black and white-Hispanic achievement gaps have been declining, albeit unsteadily. Every few years, a sample of 9-, 13-, and 17-year-olds from around the United States are given tests in math and reading as part of the National Assessment of Educational Progress (NAEP). NAEP, sometimes called "The Nation's Report Card," is designed to provide the public and ...
Reframing Educational Outcomes: Moving beyond Achievement Gaps
The biology education research community can instead broaden its sense of success to recognize the underlying historical and current contexts and the intersections of identities ... From the achievement gap to the education debt: Understanding achievement in U.S. schools. Educational Researcher, 35 (7), 3-12. 10.3102/0013189X035007003 ...
Education Gap Grows Between Rich and Poor, Studies Say
By 2007 that gap had grown to nine to one; spending by upper-income families more than doubled, while spending by low-income families grew by 20 percent. "The pattern of privileged families ...
Student achievement gap same after nearly 50 years, study says
The achievement gap between disadvantaged and well-off students is as wide today as it was for children born in 1954 when it comes to tests in math, reading, and science, researchers report in a new article for the journal Education Next.. However, the study contradicts research suggesting that socioeconomic achievement gaps have substantially widened in recent years.
Black-White Achievement Gap: Role of Race, School Urbanity, and
Recent research on Marginalization-related Diminished Returns (MDRs) has documented weaker boosting effects of parental educational attainment on educational outcomes of Black than White students. Such MDRs of parental education seem to contribute to the Black-White achievement gap.
Evidence Gap Maps in Education Research
Evidence Gap Maps in Education Research. Joshua R. Polanin Human Services Division, American Institutes for Research, Washington, District of Columbia, ... An Evidence Gap Map (EGM), a graphical or tabular visualization of systematic review and meta-analysis results, is one ideal translation technique because it provides a structured framework ...
Key findings about online learning and the homework gap amid COVID-19
America's K-12 students are returning to classrooms this fall after 18 months of virtual learning at home during the COVID-19 pandemic. Some students who lacked the home internet connectivity needed to finish schoolwork during this time - an experience often called the "homework gap" - may continue to feel the effects this school year. Here is what Pew Research Center surveys found ...
Why the gap between men and women finishing college is growing
The implications of the growing gap in educational attainment for men are significant, as research has shown the strong correlation between college completion and lifetime earnings and wealth accumulation. To explore the factors contributing to the growing gender gap in college completion, we surveyed 9,676 U.S. adults between Oct. 18-24, 2021.
There's an Education Gap Between Rural and Urban Communities. Can
Even within nations, there tends to be a yawning gap between urban and rural education outcomes. For instance, according to one 2015 standardized assessment, 15-year-olds studying in urban schools in 37 countries outperformed rural students by roughly the equivalent of one full year of schooling, even after controlling for students' socioeconomic backgrounds.
Bridging the Gap Between Research and Practice: Predicting What Will
For nearly two decades, the dominant model for evidence-based education (EBE) has focused on improving schools by researching "what works." Yet anyone familiar with EBE recognizes its relentless adversary: the gap between research and practice (Coburn & Stein, 2010; Farley-Ripple, May, Karpyn, Tilley, & McDonough, 2018; McIntyre, 2005; Nelson & Campbell, 2017; Tseng & Nutley, 2014).
The Education Gap (or Achievement Gap or Opportunity Gap) and Models of
Websites The Education Trust -- a national organization devoted to research and action to address the education/opportunity gap. Their research reports on both issues/problems AND successful schools and effective educational strategies. This site provides lots of excellent resources. Whither Opportunity? Rising Inequality, Schools and Children's Life Chances -- information and an executive ...
Rethinking the Achievement Gap
Rethinking the Achievement Gap. By Andy Porter. Visit Porter's Center on Standards, Alignment, Instruction, and Learning (C-SAIL) Back in the 1960s, the noted sociologist Christopher Jencks called for income tax redistribution to address the issue of racial inequality. Today, he looks to education: "Reducing the test score gap is probably ...
Addressing the Gap Between Education Research and Practice
Academics, after all, often seem far-removed from the daily work experience. This issue brief attempts to address the research-practice gap in the education space. First, it describes some ...
Higher Education and the Opportunity Gap
Higher Education. Economic Studies. Center for Economic Security and Opportunity. America faces an opportunity gap. Those born in the bottom ranks have difficulty moving up. Although the United ...
AI and the next digital divide in education
The existence of a " digital divide " in education—the idea that some children, families, teachers, and schools have access to information and communications technologies to support learning ...
U.S. public, private and charter schools in 5 charts
More than 4.2 million full- and part-time teachers worked at public, private and charter schools during the 2020-21 school year, the most recent year with available data. That year, about 3.5 million teachers (83%) taught at traditional public schools. Another 466,000 (11%) worked in private schools, and 251,000 (6%) taught at public charters.
The gap statement and justification in higher education research: an
Claiming a gap in the literature is the preferred rhetorical device for articulating a rationale for a study in higher education research. Explicit gap statements, which are easier to identify in the text, were a minority, and thus a careful reading was required for identifying implicit gap statements. While in many instances they were easy to ...
Key Facts, Data and Trends Since 1970
The median income of middle-class households increased from about $66,400 in 1970 to $106,100 in 2022, or 60%. Over this period, the median income of upper-income households increased 78%, from about $144,100 to $256,900. (Incomes are scaled to a three-person household and expressed in 2023 dollars.)

A decade of research on the rich-poor divide in education

Share this:

Related articles

Jill Barshay SENIOR REPORTER

Letters to the Editor

Submit a letter

Education inequalities at the school starting gate : Gaps, trends, and strategies to address them

Executive summary

What we do: Questions, data and methodology

What we find

Conclusions

Introduction: Facts about income inequality and its growth over time

How large are recent performance gaps at kindergarten entry?

How do the performance gaps in the 2010–2011 kindergarten class compare with the gaps in the prior generation?

How have the characteristics of the children in the lowest and highest SES groups changed in a generation?

How did the performance gaps between the children in the lowest and highest SES groups change in a generation?

How do child and family characteristics, activities, and expectations affect SES-based performance and performance gaps?

Sensitivity analyses: Do performance gaps vary based on which proxy for social class (socioeconomic status) is used?

What can we learn from these analyses?

What are pioneering school districts doing to combat these inequities and resulting gaps?

Introduction to the case studies: Why these districts enacted whole-child strategies

How are whole-child initiatives launched?

What do whole-child initiatives do?

How the case study districts invest in early childhood care and education

One-on-one engagement with new parents

Parenting classes

Big investments in prekindergarten programs

Strengthening the transition to kindergarten

Other investments in young children and their families

How the school districts invest in K–12 strategies to sustain and boost their early childhood investments

Enriching K–12 curricula and activities to sustain pre-K’s whole-child emphasis

Mentoring and tutoring to get and keep students engaged

Supports for student health and family wellness as a tool for sustaining early gains

How academic gains, including smaller achievement gaps, indicate that the investments are paying off

Higher rates of kindergarten readiness predict school success

Rising test scores and narrowing gaps in core academic subjects are an important sign of sustained early gains

Better student attendance and engagement are also predictors of academic gains

Increases in advanced coursework and completion of associated exams suggest improved college and career readiness

Higher graduation rates and increasing college attendance of disadvantaged students are another measure of success of comprehensive strategies

Strong parent and community engagement is another sign of progress

Expansion of these initiatives shows that other districts, and even state policymakers, consider them successful

Parents are doing what they need to do, and a growing number of communities are, too, but as a society, we are still falling far short

Next policy steps

About the authors

Acknowledgments

Figures and tables

Fully adjusted cognitive and noncognitive skills gaps between high-SES and low-SES children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010

Unadjusted cognitive and noncognitive skills gaps between high-SES and low-SES children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010, using mother's educational attainment as a proxy for socioeconomic status

Unadjusted cognitive and noncognitive skills gaps between high-SES and low-SES children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010, using household income as a proxy for socioeconomic status

Unadjusted cognitive and noncognitive skills gaps between high-SES and low-SES children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010, using number of books the child has in the home as a proxy for socioeconomic status

Reading and math achievement gaps, and principal noncognitive skills gaps between high-SES and low-SES children at the beginning of kindergarten in 2010–2011, under unadjusted and clustered models

Child and family characteristics, main developmental activities, and parental expectations for children, kindergarten classes of 1998–1999 and 2010–2011, by socioeconomic status (SES)

Reading and math skills gaps between high-SES and low-SES children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010, under unadjusted to fully adjusted models

Noncognitive skills gaps between high-SES and low-SES children at the beginning of kindergarten in 1998 and change in gaps by the beginning of kindergarten in 2010, under unadjusted to fully adjusted models

Reductions in skills gaps between high-SES and low-SES children after accounting for missingness and covariates, 1998 and 2010

Summary of association between cognitive and noncognitive skills at kindergarten entry and selected early educational practices, fully adjusted differences (Model 4)

Cognitive and noncognitive skills gaps between high-SES and low-SES children using mother's educational attainment as a proxy for socioeconomic status (SES), under unadjusted and fully adjusted models

Cognitive and noncognitive skills gaps between high-SES and low-SES children using household income as a proxy for socioeconomic status (SES), under unadjusted and fully adjusted models

Cognitive and noncognitive skills gaps between high-SES and low-SES children using number of books child has in the home as a proxy for socioeconomic status, under unadjusted and fully adjusted models

'Whole-child' case study initiatives, by service area

Appendix A. Data

Cognitive skills

Principal noncognitive skills

Appendix B. Methodology

Appendix C. Sensitivity analysis (I): Multiple imputation

Appendix D. Sensitivity analysis (II): The different scores available in ECLS-K and the sensitivity of the results to changing them

The scores: Which one to use and definitions

The analyses

Other considerations

Appendix E. Descriptions of 12 community-level whole-child education initiatives

Boston, Massachusetts

Durham, North Carolina

Minneapolis, Minnesota

New York, New York

Orange County, Florida

Initiatives that serve all of a school district

Kalamazoo, Michigan

Montgomery County, Maryland

Pea Ridge, Arkansas

Vancouver, Washington