articles about education inequality

This is what the racial education gap in the US looks like right now

Racial achievement gaps in the United States has been slow and unsteady. Image:  Unsplash/Santi Vedrí

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Stay up to date:.

• 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 part of The Jobs Reset Summit .

In the United States today, the average Black and Hispanic students are about three years ahead of where their parents were in maths skills.

They’re roughly two to three years ahead of them in reading, too.

And while white students’ test scores in these subjects have also improved, they’re not rising by as much. This means racial achievement gaps – a key way of monitoring whether all students have access to a good education – in the country are narrowing, research by Stanford University shows.

But while the trend suggests progress is being made in improving racial educational disparities, it doesn’t show the full picture. Progress, the university says, has been slow and uneven.

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Standardized tests

Stanford’s Educational Opportunity Monitoring Project uses average standardized test scores for nine-, 13- and 17-year-olds to measure these achievement gaps.

It’s able to do this because the same tests have been used by the National Assessment of Educational Progress to observe maths and reading skills since the 1970s.

In the following decades, as the above chart shows, achievement gaps have significantly declined in all age groups and in both maths and reading. But it’s been something of a roller-coaster.

Substantial progress stalled at the end of the 1980s and throughout the 1990s and in some cases the gaps grew larger. Since then, they’ve been declining steadily and are now significantly smaller than they were in the 1970s.

But these gaps are still “very large”. In fact, the difference in standardized test scores between white and Black students currently amounts to roughly two years of education. And the gap between white and Hispanic students is almost as big.

Schools not to blame

This disparity exists across the US. Racial achievement gaps have narrowed in most states – although they’ve widened in a small number. In almost all of the country’s 100 largest school districts , though, there’s a big achievement gap between white and Black students.

So why is this? Stanford says its data doesn’t support the common argument that schools themselves are to blame for low average test scores, which is often made because white students tend to live in wealthier communities where schools are presumed to be better.

In fact, it says, the scores actually represent gaps in educational opportunity, which can be traced back to a child’s early experiences. These experiences are formed at home, in childcare and preschool, and in communities – and they provide opportunities to develop socioemotional and academic capacities.

Higher-income families are more likely to be able to provide these opportunities to their children, so a family’s socioeconomic resources are strongly related to educational outcomes , Stanford says. It notes that in the US, Black and Hispanic children’s parents typically have lower incomes and levels of educational attainment than those of white children.

Other factors, such as patterns of residential and school segregation and a state’s educational and social policies, could also have a role in the size of achievement gaps.

And discipline could play its part, too, according to another Stanford study. It linked the achievement gap between Black and white students to the fact that the former are punished more harshly for similar misbehaviour, for example being more likely to be suspended from school than the latter.

Long-term effects

Stanford says using data to map race and poverty could provide the insights needed to help improve educational opportunity for all children.

And this kind of insight is needed now more than ever. The school shutdowns forced by COVID-19 could have exacerbated existing achievement gaps , according to research from McKinsey. The consultancy says the resulting learning losses – predicted to be greater for low-income Black and Hispanic students – could have long-term effects on the economic well-being of the affected children.

Black and Hispanic families are less likely to have high-speed internet at home, making distance learning difficult. And students living in low-income neighbourhoods are less likely to have had decent home schooling, according to the Economic Policy Institute. Earlier in the pandemic, it said coronavirus would "explode" achievement gaps , suggesting it could expand them by the equivalent of another half a year of schooling.

The World Economic Forum’s Jobs Reset Summit brings together leaders from business, government, civil society, media and the broader public to shape a new agenda for growth, jobs, skills and equity.

The two-day virtual event, being held on 1-2 June 2021, will address the most critical areas of debate, articulate pathways for action, and mobilize the most influential leaders and organizations to work together to accelerate progress.

The Summit will develop new frameworks, shape innovative solutions and accelerate action on four thematic pillars: Economic Growth, Revival and Transformation; Work, Wages and Job Creation; Education, Skills and Lifelong Learning; and Equity, Inclusion and Social Justice.

The World Economic Forum will be exploring the issues around growing income inequality, and what to do about it, as part of The Jobs Reset Summit .

The summit will look at ways to shape more inclusive, fair and sustainable organizations, economies and societies as we emerge from the current crisis.

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The Origins of Racial Inequality in Education

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To understand and address educational inequality today, everyone involved in public schools must first be aware of how inequality has been embedded in the foundations of the country’s education system.

That’s the premise of one of five reports from Columbia University on the origins of racial inequality in the United States, published on March 20.

“ Uncovering Inequality ” is a research-based project spearheaded by Jelani Cobb, dean of Columbia Journalism School, and the university’s Ira A. Lipman Center for Journalism and Civil and Human Rights. Conceived in the aftermath of George Floyd’s murder, the project covers housing, criminal justice, health, economics, and education, highlighting how public policies have, by design, created and furthered racial inequality.

The main goal is to ensure that media conversations and coverage on these topics are rooted in historical context, with the hope that such information could move the needle toward addressing systemic inequality, whether through policy changes or more nuanced conversations, Cobb told Education Week.

But those working in K-12 education could also benefit from the education report in this project by seeing how topics intersect—such as the relationship between inequitable housing policies and educational inequity—and diving deeper into the origins of the work they do, said Juontel White, senior vice president of programs and advocacy with the Schott Foundation for Public Education, and co-author of the education report.

“I think having that historical grounding is helpful in the engagement with multiple stakeholders that educators face in their day to day work,” White told Education Week.

What the report offers

The education portion of the project covers a chronology of education policies following the Supreme Court decision of Brown v. Board of Education of Topeka in 1954 and how their foundations and implementation created or contributed to racial inequalities. That education analysis includes an acknowledgment of how schooling during the pandemic shed light on this history.

“In more ways than one, the schooling experiences of students of color during the COVID-19 pandemic have illustrated that contemporary educational inequality is inextricably linked with the history of education, and other sectors such as public health and housing in the United States,” the report reads.

It covers the early days of schooling, roughly starting with the 1800s “common school” system of universal schooling funded by local taxes—and the disparate experiences among various racial/ethnic groups at the time when it came to education access and quality.

From there, the report explores the national patchwork of desegregation court orders following Brown ; the resegregation that emerged years later, and the topic of school choice in the 1990s; the relationship between school and neighborhood segregation; the role federal funding policies and high-stakes testing play in furthering racial inequities in education; and the question of inequalities in terms of school curriculum—namely whose history is taught in class, and whose is excluded or sidelined.

That last topic, curriculum, is particularly pertinent to educators facing legal restrictions in teaching about certain aspects of U.S. history. In at least 18 states , educators are banned in how they can discuss topics of race and racism.

This reality is not lost on the project writers.

“One of the highlights of the report is that the very content that is being politicized currently has never, in its totality, been a part of the fabric of public education curriculum,” White said.

It’s partly why researchers such as Eric Duncan, director for P-12 policy at The Education Trust, praise this report for offering teachers context they lack from their own experience as students.

“You can’t expect that our teaching population who have gone through schooling in America would understand this context, because it’s not taught in traditional settings,” Duncan said.

Why educators need to know education history

For years now, school districts, education researchers, and nonprofits have devoted time, money, and personnel to highlighting and attempting to dismantle inequalities in public education.

The historical context of how systems were created, for whom, and by whom, is key to this work, Duncan said.

For instance, debates around affirmative action in university enrollment need to factor in the issue of legacy admissions, in which the children of graduates are given preferential treatment. Because of past laws banning admission to Black students’ ancestors, these descendants are limited in their eligibility for legacy consideration, Duncan said.

And research has shown that “raising awareness of systemic inequities and their effects can foster empathy and lower explicit and implicit biases toward marginalized groups, whether among school administrators, teachers, or others,” said Felice J. Levine, executive director of the American Educational Research Association, or AERA.

“Every individual involved in public education—as a teacher, administrator, parent, or taxpayer—should be fully aware of the history and persistent ramifications of the racial inequity ingrained in our system,” Levine said.

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Recognizing and Overcoming Inequity in Education

About the author, sylvia schmelkes.

Sylvia Schmelkes is Provost of the Universidad Iberoamericana in Mexico City.

22 January 2020 Introduction

I nequity is perhaps the most serious problem in education worldwide. It has multiple causes, and its consequences include differences in access to schooling, retention and, more importantly, learning. Globally, these differences correlate with the level of development of various countries and regions. In individual States, access to school is tied to, among other things, students' overall well-being, their social origins and cultural backgrounds, the language their families speak, whether or not they work outside of the home and, in some countries, their sex. Although the world has made progress in both absolute and relative numbers of enrolled students, the differences between the richest and the poorest, as well as those living in rural and urban areas, have not diminished. 1

These correlations do not occur naturally. They are the result of the lack of policies that consider equity in education as a principal vehicle for achieving more just societies. The pandemic has exacerbated these differences mainly due to the fact that technology, which is the means of access to distance schooling, presents one more layer of inequality, among many others.

The dimension of educational inequity

Around the world, 258 million, or 17 per cent of the world’s children, adolescents and youth, are out of school. The proportion is much larger in developing countries: 31 per cent in sub-Saharan Africa and 21 per cent in Central Asia, vs. 3 per cent in Europe and North America. 2  Learning, which is the purpose of schooling, fares even worse. For example, it would take 15-year-old Brazilian students 75 years, at their current rate of improvement, to reach wealthier countries’ average scores in math, and more than 260 years in reading. 3 Within countries, learning results, as measured through standardized tests, are almost always much lower for those living in poverty. In Mexico, for example, 80 per cent of indigenous children at the end of primary school don’t achieve basic levels in reading and math, scoring far below the average for primary school students. 4

The causes of educational inequity

There are many explanations for educational inequity. In my view, the most important ones are the following:

• Equity and equality are not the same thing. Equality means providing the same resources to everyone. Equity signifies giving more to those most in need. Countries with greater inequity in education results are also those in which governments distribute resources according to the political pressure they experience in providing education. Such pressures come from families in which the parents attended school, that reside in urban areas, belong to cultural majorities and who have a clear appreciation of the benefits of education. Much less pressure comes from rural areas and indigenous populations, or from impoverished urban areas. In these countries, fewer resources, including infrastructure, equipment, teachers, supervision and funding, are allocated to the disadvantaged, the poor and cultural minorities.
• Teachers are key agents for learning. Their training is crucial.  When insufficient priority is given to either initial or in-service teacher training, or to both, one can expect learning deficits. Teachers in poorer areas tend to have less training and to receive less in-service support.
• Most countries are very diverse. When a curriculum is overloaded and is the same for everyone, some students, generally those from rural areas, cultural minorities or living in poverty find little meaning in what is taught. When the language of instruction is different from their native tongue, students learn much less and drop out of school earlier.
• Disadvantaged students frequently encounter unfriendly or overtly offensive attitudes from both teachers and classmates. Such attitudes are derived from prejudices, stereotypes, outright racism and sexism. Students in hostile environments are affected in their disposition to learn, and many drop out early.

It doesn’t have to be like this

When left to inertial decision-making, education systems seem to be doomed to reproduce social and economic inequity. The commitment of both governments and societies to equity in education is both necessary and possible. There are several examples of more equitable educational systems in the world, and there are many subnational examples of successful policies fostering equity in education.

Why is equity in education important?

Education is a basic human right. More than that, it is an enabling right in the sense that, when respected, allows for the fulfillment of other human rights. Education has proven to affect general well-being, productivity, social capital, responsible citizenship and sustainable behaviour. Its equitable distribution allows for the creation of permeable societies and equity. The 2030 Agenda for Sustainable Development includes Sustainable Development Goal 4, which aims to ensure “inclusive and equitable quality education and promote lifelong learning opportunities for all”. One hundred eighty-four countries are committed to achieving this goal over the next decade. 5  The process of walking this road together has begun and requires impetus to continue, especially now that we must face the devastating consequences of a long-lasting pandemic. Further progress is crucial for humanity.

Notes  1 United Nations Educational, Scientific and Cultural Organization , Inclusive Education. All Means All , Global Education Monitoring Report 2020 (Paris, 2020), p.8. Available at https://en.unesco.org/gem-report/report/2020/inclusion . 2 Ibid., p. 4, 7. 3 World Bank Group, World Development Report 2018: Learning to Realize Education's Promise (Washington, DC, 2018), p. 3. Available at https://www.worldbank.org/en/publication/wdr2018 .  4 Instituto Nacional para la Evaluación de la Educación, "La educación obligatoria en México", Informe 2018 (Ciudad de México, 2018), p. 72. Available online at https://www.inee.edu.mx/wp-content/uploads/2018/12/P1I243.pdf . 5 United Nations Educational, Scientific and Cultural Organization , “Incheon Declaration and Framework for Action for the implementation of Sustainable Development Goal 4” (2015), p. 23. Available at  https://iite.unesco.org/publications/education-2030-incheon-declaration-framework-action-towards-inclusive-equitable-quality-education-lifelong-learning/   The UN Chronicle  is not an official record. It is privileged to host senior United Nations officials as well as distinguished contributors from outside the United Nations system whose views are not necessarily those of the United Nations. Similarly, the boundaries and names shown, and the designations used, in maps or articles do not necessarily imply endorsement or acceptance by the United Nations.   

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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

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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.

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)

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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

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

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

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

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

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)

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

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

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

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)

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

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

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.

*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).

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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.

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

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)

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

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

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 related work on Student achievement | Education | Educational inequity | Children | Economic inequality | Inequality and Poverty | Early childhood

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Increased years of schooling have been linked to better health and survival 1 , slower population growth 2 and greater economic growth 3 . Because of its importance, access to “inclusive and equitable quality education” was included as one of the Sustainable Development Goals (SDGs) ratified by the United Nations General Assembly in 2015 (see go.nature.com/3ana8ob ). The SDGs are an ambitious set of international development targets to be achieved by 2030. Writing in Nature , Friedman et al. 4 provide evidence that, although most nations are projected to achieve near-universal primary education by 2030, large inter-regional disparities in the rates of secondary-school completion will persist.

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Why Access to Education is Key to Systemic Equality

All students have a right to an equal education, but students of color — particularly Black and Brown students and students with disabilities, have historically been marginalized and criminalized by the public school system. The ACLU has been working to challenge unconstitutional disciplinary policies in schools, combat classroom censorship efforts that disproportionately impact marginalized students, and support race conscious admission policies to increase access to higher education.

Let’s break down why education equity is critical to the fight for systemic equality.

What does “education equity” mean, and why is it a civil rights issue?

Education equity means all students have equal access to a high quality education, safe learning environment, and a diverse student body that enriches the educational experiences of all students.

As the Supreme Court said in Brown v. Board of Education , education “is the very foundation of good citizenship.” Through education, young people learn important values about our culture and democratic society, and about their own values and relationships to others in this society. In addition to being an important foundation for kids’ and young adults’ future professional success, education allows individuals to be informed voters and participants in democratic processes, and public education is the first experience most people will have with the government.

For all of these reasons, equity in education is a critical foundation for a democratic society in which people of all backgrounds are equally included. Without equal opportunities to obtain an education, they will not be able to participate equally in jobs, in voting, and in other crucial areas of life. And when students are not able to learn together, this harms their ability to work together and live and engage with one another later in life.

What was the foundational Supreme Court case aimed at addressing discrimination in education nationwide?

Modern understandings of educational equity have their roots in Brown v. Board of Education , the 1954 landmark Supreme Court decision that ordered an end to school segregation and held racial segregation in education violates the Equal Protection Clause of the constitution. The ACLU played an important role in the Brown litigation, and has continued to fight for education equity on many fronts in the decades since.

What is the “school-to-prison pipeline”?

The school-to-prison pipeline refers to school discipline practices, such as suspensions and referrals to law enforcement, that funnel youth out of the classroom and into the juvenile and criminal legal systems.

This trend reflects our country’s prioritization of incarceration over education, and it’s made worse as resources for public schools are cut. From inadequate resources for counseling to an overreliance on school-based police officers to enforce harsh zero-tolerance policies, many students — overwhelmingly students of color and students with disabilities — are isolated, punished, and pushed out of our education system for typical childish behavior and behaviors associated with disabilities.

Cops and No Counselors

How the lack of school mental health staff is harming students.

Source: American Civil Liberties Union

Even a single suspension or disciplinary infraction can have enormous consequences for a child’s education. As a student is pushed further down the school-to-prison pipeline, those consequences escalate quickly. In some jurisdictions, students who have been suspended or expelled have no right to an education at all. In others, they are sent to disciplinary alternative schools.Youth who become involved in the juvenile system are often denied procedural protections in the courts, and students pushed along the pipeline find themselves in juvenile detention facilities, many of which provide few, if any, educational services.

How are Black students, students of color, and students with disabilities disproportionately impacted by discrimination in education? What barriers to higher education exist for students of color?

Black and Brown students and students with disabilities are disproportionately subjected to discipline and referrals to law enforcement that remove them from the classroom and subject them to additional punitive consequences and even physical injury. For example, over the 2017-2018 school year, Black students accounted for 28.7 percent of all students referred to law enforcement and 31.6 percent of all students arrested at school or during a school-related activity — despite representing just 15.1 percent of the total enrolled student population.

Our country’s schools are increasingly diverse, but also increasingly segregated . Students of all races are harmed by the inability to learn with one another in diverse school settings. Black and Latine students are also more likely to attend schools that are intensely segregated both by race and by socioeconomic status. Students of color are also less likely to have access to advanced courses, and are frequently tracked away from college preparatory courses when they do exist.

Moving Beyond the Supreme Court’s Affirmative Action Rulings

The work to ensure educational opportunities for people of color continues, despite the court’s decision.

Inequities in K-12 education can be replicated in college and university admissions criteria. As with elementary and secondary schools, colleges and universities are required to ensure that educational opportunities are open to all students from the application stage and through student’s experiences during their college education. There are a wide range of things that colleges and universities can do to ensure that educational opportunities are open to people of all backgrounds.

What non-punitive responses should schools take when approaching school discipline issues? What non-punitive resources should schools invest in?

There are a range of evidence-based methods schools can use to respond to the behavioral needs of students. These range from strategies that teachers and schools can use to foster a positive learning culture and model, to interventions addressing particular disciplinary issues, such as conflict de-escalation or restorative justice, to using functional behavioral assessments and wraparound support for those students with higher levels of need.

Additionally, schools that employed more mental health providers saw improved student engagement and graduation rates . Schools that used other types of support, including restorative and trauma-informed practices, saw beneficial results, including reduced disciplinary incidents, suspensions, dropouts, and expulsions. Investing in mental health resources, support personnel, and interventions that promote positive student interactions can make schools safer and healthier learning environments, while also helping to combat the discriminatory school-to-prison pipeline that targets students of color and students with disabilities.

How do classroom censorship efforts (i.e. laws that block students and teachers from talking and learning about race and gender) lead to inequality in education?

Instruction about racism and sexism belongs in schools because it equips students to process the world around them and to live in a multicultural society.

Attacks on education have morphed from demands to exclude critical race theory from classrooms to ever-increasingly devious and dangerous demands to erase entire concepts from American history. Book bans, so-called transparency laws designed to intimidate educators into compliance, and attacks on individual expression have left our education system at the mercy of a hostile and discriminatory minority. Students can’t learn in that type of environment. Our future depends on educational institutions that value instruction about systemic racism and sexism. We need to expand culturally relevant instruction and increase funding for diversity, equity, and inclusion in schools, not attack it for its role in uplifting the systematically oppressed.

What can colleges do to ensure they create opportunities for students of color in light of the recent Supreme Court decision effectively eliminating the use of affirmative action in college admissions?

Affirmative action in college admissions has been an important tool, but it is not the only avenue for ensuring that educational opportunities are open to all. In the absence of affirmative action, it is more important than ever that schools work to identify and remove inequitable barriers to higher education. At a minimum, schools must continue to comply with federal and state civil rights laws that require them to provide educational opportunities on an equal basis. They can achieve this by ensuring that policies and practices do not unnecessarily limit opportunities for people on the basis of race or ethnicity (or other protected characteristics, including disability, sex, sexual orientation, and gender identity) and by ensuring that school climate enables all students to access and engage with educational opportunities .

What does the ACLU’s work in education equity look like today?

The ACLU and our affiliates around the country are challenging disciplinary policies that disparately target students of color and students with disabilities and infringe on their right to a safe learning environment. This includes litigation, such as our recent victory resulting in the end to charging students with “disorderly conduct” or “disturbing schools” in South Carolina schools, and advocacy, such as the ACLU of Idaho’s recent report Proud to be Brown and the related civil rights complaint. The report documents how school districts in Idaho are jeopardizing Latine students’ civil rights and liberties by enforcing “gang” dress codes that target mostly Latine students in a discriminatory way, and have negative consequences on their cultural identity, discipline, and education.

CYAP v. Wilson

The ACLU Union filed a federal lawsuit challenging South Carolina’s “disturbing schools” law.

We are also fighting back against efforts to ban books and restrict what students can learn about race, gender, and sexual orientation. In Florida, for example, we’re challenging the state’s harmful Stop WOKE Act. We continue to press for equity in higher education following the Supreme Court’s ruling on affirmative action, and defend against attacks on diversity in K-12 schools.

From K-12 to higher education, the ACLU is working to combat discrimination in education and ensure all people have equal access to safe, quality education.

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The U.S. student population is more diverse, but schools are still highly segregated

Sequoia Carrillo

Pooja Salhotra

The U.S. student body is more diverse than ever before. Nevertheless, public schools remain highly segregated along racial, ethnic and socioeconomic lines.

That's according to a report released Thursday by the U.S. Government Accountability Office (GAO). More than a third of students (about 18.5 million of them) attended a predominantly same-race/ethnicity school during the 2020-21 school year, the report finds. And 14% of students attended schools where almost all of the student body was of a single race/ethnicity.

The report is a follow up to a 2016 GAO investigation on racial disparity in K-12 schools. That initial report painted a slightly worse picture, but findings from the new report are still concerning, says Jackie Nowicki, the director of K-12 education at the GAO and lead author of the report.

Why White School Districts Have So Much More Money

"There is clearly still racial division in schools," says Nowicki. She adds that schools with large proportions of Hispanic, Black and American Indian/Alaska Native students – minority groups with higher rates of poverty than white and Asian American students – are also increasing. "What that means is you have large portions of minority children not only attending essentially segregated schools, but schools that have less resources available to them."

"There are layers of factors here," she says. "They paint a rather dire picture of the state of schooling for a segment of the school-age population that federal laws were designed to protect."

School segregation happens across the country

Segregation has historically been associated with the Jim Crow laws of the South. But the report finds that, in the 2020-21 school year, the highest percentage of schools serving a predominantly single-race/ethnicity student population – whether mostly white, mostly Hispanic or mostly Black etc. – were in the Northeast and the Midwest.

School segregation has "always been a whole-country issue," says U.S. Rep. Bobby Scott, D-Va., who heads the House education and labor committee. He commissioned both the 2016 and 2022 reports. "The details of the strategies may be different, but during the '60s and '70s, when the desegregation cases were at their height, cases were all over the country."

How The Systemic Segregation Of Schools Is Maintained By 'Individual Choices'

The GAO analysis also found school segregation across all school types, including traditional public schools, charter schools and magnet schools. Across all charter schools, which are publicly funded but privately run, more than a third were predominantly same-race/ethnicity, serving mostly Black and Hispanic students.

There's history behind the report's findings

Nowicki and her team at the GAO say they were not surprised by any of the report's findings. They point to historical practices, like redlining , that created racially segregated neighborhoods.

And because 70% of U.S. students attend their neighborhood public schools, Nowicki says, racially segregated neighborhoods have historically made for racially segregated schools.

The 50 Most Segregating School Borders In America

"There are historical reasons why neighborhoods look the way they look," she explains. "And some portion of that is because of the way our country chose to encourage or limit where people could live."

Though the 1968 Fair Housing Act outlawed housing discrimination on the basis of race, the GAO says that in some states, current legislation reinforces racially isolated communities.

"Our analysis showed that predominantly same-race/ethnicity schools of different races/ethnicities exist in close proximity to one another within districts, but most commonly exist among neighboring districts," the report says.

School district secessions have made segregation worse

One cause for the lack of significant improvement, according to the GAO, is a practice known as district secession, where schools break away from an existing district – often citing a need for more local control – and form their own new district. The result, the report finds, is that segregation deepens.

"In the 10 years that we looked at district secessions, we found that, overwhelmingly, those new districts were generally whiter, wealthier than the remaining districts," Nowicki says.

Six of the 36 district secessions identified in the report happened in Memphis, Tenn., which experienced a historic district merger several years ago. Memphis City Schools, which served a majority non-white student body, dissolved in 2011 due to financial instability. It then merged with the neighboring district, Shelby County Schools, which served a wealthier, majority white population.

This Supreme Court Case Made School District Lines A Tool For Segregation

Joris Ray was a Memphis City Schools administrator at the time of the merger. He recalls that residents of Shelby County were not satisfied with the new consolidated district. They successfully splintered off into six separate districts.

As a result, the GAO report says, racial and socioeconomic segregation has grown in and around Memphis. All of the newly formed districts are whiter and wealthier than the one they left, which is now called Memphis-Shelby County Schools.

Why Busing Didn't End School Segregation

"This brings negative implications for our students overall," says Ray, who has led Memphis-Shelby County Schools since 2019. "Research has shown that students in more diverse schools have lower levels of prejudice and stereotypes and are more prepared for top employers to hire an increasingly diverse workforce."

The GAO report finds that this pattern – of municipalities removing themselves from a larger district to form their own, smaller school district – almost always creates more racial and socioeconomic segregation. Overall, new districts tend to have larger shares of white and Asian American students, and lower shares of Black and Hispanic students, the report finds. New districts also have significantly fewer students eligible for free or reduced-price lunch, a common measure of poverty.

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Four of the biggest problems facing education—and four trends that could make a difference

Eduardo velez bustillo, harry a. patrinos.

In 2022, we published, Lessons for the education sector from the COVID-19 pandemic , which was a follow up to,  Four Education Trends that Countries Everywhere Should Know About , which summarized views of education experts around the world on how to handle the most pressing issues facing the education sector then. We focused on neuroscience, the role of the private sector, education technology, inequality, and pedagogy.

Unfortunately, we think the four biggest problems facing education today in developing countries are the same ones we have identified in the last decades .

1. The learning crisis was made worse by COVID-19 school closures

Low quality instruction is a major constraint and prior to COVID-19, the learning poverty rate in low- and middle-income countries was 57% (6 out of 10 children could not read and understand basic texts by age 10). More dramatic is the case of Sub-Saharan Africa with a rate even higher at 86%. Several analyses show that the impact of the pandemic on student learning was significant, leaving students in low- and middle-income countries way behind in mathematics, reading and other subjects.  Some argue that learning poverty may be close to 70% after the pandemic , with a substantial long-term negative effect in future earnings. This generation could lose around $21 trillion in future salaries, with the vulnerable students affected the most.

2. Countries are not paying enough attention to early childhood care and education (ECCE)

At the pre-school level about two-thirds of countries do not have a proper legal framework to provide free and compulsory pre-primary education. According to UNESCO, only a minority of countries, mostly high-income, were making timely progress towards SDG4 benchmarks on early childhood indicators prior to the onset of COVID-19. And remember that ECCE is not only preparation for primary school. It can be the foundation for emotional wellbeing and learning throughout life; one of the best investments a country can make.

3. There is an inadequate supply of high-quality teachers

Low quality teaching is a huge problem and getting worse in many low- and middle-income countries.  In Sub-Saharan Africa, for example, the percentage of trained teachers fell from 84% in 2000 to 69% in 2019 . In addition, in many countries teachers are formally trained and as such qualified, but do not have the minimum pedagogical training. Globally, teachers for science, technology, engineering, and mathematics (STEM) subjects are the biggest shortfalls.

4. Decision-makers are not implementing evidence-based or pro-equity policies that guarantee solid foundations

It is difficult to understand the continued focus on non-evidence-based policies when there is so much that we know now about what works. Two factors contribute to this problem. One is the short tenure that top officials have when leading education systems. Examples of countries where ministers last less than one year on average are plentiful. The second and more worrisome deals with the fact that there is little attention given to empirical evidence when designing education policies.

To help improve on these four fronts, we see four supporting trends:

1. Neuroscience should be integrated into education policies

Policies considering neuroscience can help ensure that students get proper attention early to support brain development in the first 2-3 years of life. It can also help ensure that children learn to read at the proper age so that they will be able to acquire foundational skills to learn during the primary education cycle and from there on. Inputs like micronutrients, early child stimulation for gross and fine motor skills, speech and language and playing with other children before the age of three are cost-effective ways to get proper development. Early grade reading, using the pedagogical suggestion by the Early Grade Reading Assessment model, has improved learning outcomes in many low- and middle-income countries. We now have the tools to incorporate these advances into the teaching and learning system with AI , ChatGPT , MOOCs and online tutoring.

2. Reversing learning losses at home and at school

There is a real need to address the remaining and lingering losses due to school closures because of COVID-19.  Most students living in households with incomes under the poverty line in the developing world, roughly the bottom 80% in low-income countries and the bottom 50% in middle-income countries, do not have the minimum conditions to learn at home . These students do not have access to the internet, and, often, their parents or guardians do not have the necessary schooling level or the time to help them in their learning process. Connectivity for poor households is a priority. But learning continuity also requires the presence of an adult as a facilitator—a parent, guardian, instructor, or community worker assisting the student during the learning process while schools are closed or e-learning is used.

To recover from the negative impact of the pandemic, the school system will need to develop at the student level: (i) active and reflective learning; (ii) analytical and applied skills; (iii) strong self-esteem; (iv) attitudes supportive of cooperation and solidarity; and (v) a good knowledge of the curriculum areas. At the teacher (instructor, facilitator, parent) level, the system should aim to develop a new disposition toward the role of teacher as a guide and facilitator. And finally, the system also needs to increase parental involvement in the education of their children and be active part in the solution of the children’s problems. The Escuela Nueva Learning Circles or the Pratham Teaching at the Right Level (TaRL) are models that can be used.

3. Use of evidence to improve teaching and learning

We now know more about what works at scale to address the learning crisis. To help countries improve teaching and learning and make teaching an attractive profession, based on available empirical world-wide evidence , we need to improve its status, compensation policies and career progression structures; ensure pre-service education includes a strong practicum component so teachers are well equipped to transition and perform effectively in the classroom; and provide high-quality in-service professional development to ensure they keep teaching in an effective way. We also have the tools to address learning issues cost-effectively. The returns to schooling are high and increasing post-pandemic. But we also have the cost-benefit tools to make good decisions, and these suggest that structured pedagogy, teaching according to learning levels (with and without technology use) are proven effective and cost-effective .

4. The role of the private sector

When properly regulated the private sector can be an effective education provider, and it can help address the specific needs of countries. Most of the pedagogical models that have received international recognition come from the private sector. For example, the recipients of the Yidan Prize on education development are from the non-state sector experiences (Escuela Nueva, BRAC, edX, Pratham, CAMFED and New Education Initiative). In the context of the Artificial Intelligence movement, most of the tools that will revolutionize teaching and learning come from the private sector (i.e., big data, machine learning, electronic pedagogies like OER-Open Educational Resources, MOOCs, etc.). Around the world education technology start-ups are developing AI tools that may have a good potential to help improve quality of education .

After decades asking the same questions on how to improve the education systems of countries, we, finally, are finding answers that are very promising.  Governments need to be aware of this fact.

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Consultant, Education Sector, World Bank

Senior Adviser, Education

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Exploring Equity: Race and Ethnicity

• Posted February 18, 2021
• By Gianna Cacciatore
• Diversity, Equity, and Inclusion
• Inequality and Education Gaps
• Moral, Civic, and Ethical Education
• Teachers and Teaching

The history of education in the United States is rife with instances of violence and oppression along lines of race and ethnicity. For educators, leading conversations about race and racism is a challenging, but necessary, part of their work.

“Schools operate within larger contexts: systems of race, racism, and white supremacy; systems of migration and ethnic identity formation; patterns of socialization; the changing realities of capitalism and politics,” explains historian and Harvard lecturer Timothy Patrick McCarthy , co-faculty lead of Race and Ethnicity in Context, a new module offered at the Harvard Graduate School of Education this January as part of a pilot of HGSE’s Equity and Opportunity Foundations course. “How do we understand the role that racial and ethnic identity play with respect to equity and opportunity within an educational context?”

>> Learn more about Equity and Opportunity and HGSE’s other foundational learning experiences.

For educators exploring question in their own homes, schools, and communities, McCarthy and co-faculty lead Ashley Ison, an HGSE doctoral student, offer five ways to get started.

1.    Begin with the self.

Practitioners enter conversations about race and racism from different backgrounds, with different lived experiences, personal and professional perspectives, and funds of knowledge in their grasps. Given diverse contexts and realities, it is important that leaders encourage personal transformation and growth. Educators should consider how race and racism, as well as racial and ethnic identity formation, impact their lives as educational professionals, as parents, and as policymakers – whatever roles they hold in society. “This is personal work, but that personal work is also political work,” says Ison.

2.    Model vulnerability.

Entering into discussions of race and racism can be challenging, even for those with experience in this work. A key part of enabling participants to lean into the challenge is being vulnerable. “You have trust your students,” explains McCarthy. “Part of that is modeling authentic vulnerability and proximity to the work.” This can be done by modeling discussion skills, like sharing the space and engaging directly with the comments of other participants, as well as by opening up personally to participants.  

“Fear can impact how people feel talking about race and ethnicity in an inter-group space,” says Ison. Courage, openness, and trust are key to overcoming that fear and enabling listening, which ultimately allows for critical thinking and change.

3.    Be transparent.

Part of being vulnerable is being fully transparent with your students from day one. “Intentions are important,” explains McCarthy. “The gap between intention and impact is often rooted in a lack of transparency about where you’re coming from or where you are hoping to go.”

4.    Center voices of color.

Voice and story are powerful tools in this work. Leaders must consider whose voices and stories take precedence on the syllabus. “Consider highlighting authors of color, in particular, who are thinking and writing about these issues,” says Ison. Becoming familiar with a variety of perspectives can help practitioners understand the voices and ideas that exist, she explains.

“Voice and storytelling can bear witness to the various kinds of systematic injustices and inequities we are looking at, but they also function as sources of power for imagining and reimagining the world we are trying to build, all while providing a deeper knowledge of the world as it has existed historically,” adds McCarthy.

5.    Prioritize discussion and reflection.

Since this work is as much about critical thinking as it is about content, it is important for educators to make space for discussion and reflection, at the whole-class, small-group, and individual levels. Ison and McCarthy encourage educators to allow students to generate and guide the discussion of predetermined course materials. They also recommend facilitating small group reflections that may spark conversation that can extend into other spaces outside of the classroom.

Selected Resources:

• Poor, but Privileged
• NPR: "The Importance of Diversity in Teaching Staff”
• TED Talk with Clint Smith: "The Danger of Silence"

More Stories from the Series:

• Exploring Equity: Citizenship and Nationality
• Exploring Equity: Gender and Sexuality
• Exploring Equity: Dis/ability
• Exploring Equity: Class

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

When there’s inequity in learning, it’s usually baked into life, Harvard analysts say

Corydon Ireland

Harvard Correspondent

Third in a series on what Harvard scholars are doing to identify and understand inequality, in seeking solutions to one of America’s most vexing problems.

Before Deval Patrick ’78, J.D. ’82, was the popular and successful two-term governor of Massachusetts, before he was managing director of high-flying Bain Capital, and long before he was Harvard’s most recent Commencement speaker , he was a poor black schoolchild in the battered housing projects of Chicago’s South Side.

The odds of his escaping a poverty-ridden lifestyle, despite innate intelligence and drive, were long. So how did he help mold his own narrative and triumph over baked-in societal inequality ? Through education.

“Education has been the path to better opportunity for generations of American strivers, no less for me,” Patrick said in an email when asked how getting a solid education, in his case at Milton Academy and at Harvard, changed his life.

“What great teachers gave me was not just the skills to take advantage of new opportunities, but the ability to imagine what those opportunities could be. For a kid from the South Side of Chicago, that’s huge.”

If inequality starts anywhere, many scholars agree, it’s with faulty education. Conversely, a strong education can act as the bejeweled key that opens gates through every other aspect of inequality , whether political, economic , racial, judicial, gender- or health-based.

Simply put, a top-flight education usually changes lives for the better. And yet, in the world’s most prosperous major nation, it remains an elusive goal for millions of children and teenagers.

Plateau on educational gains

The revolutionary concept of free, nonsectarian public schools spread across America in the 19th century. By 1970, America had the world’s leading educational system, and until 1990 the gap between minority and white students, while clear, was narrowing.

But educational gains in this country have plateaued since then, and the gap between white and minority students has proven stubbornly difficult to close, says Ronald Ferguson, adjunct lecturer in public policy at Harvard Kennedy School (HKS) and faculty director of Harvard’s Achievement Gap Initiative. That gap extends along class lines as well.

“What great teachers gave me was not just the skills to take advantage of new opportunities, but the ability to imagine what those opportunities could be. For a kid from the South Side of Chicago, that’s huge.” — Deval Patrick

In recent years, scholars such as Ferguson, who is an economist, have puzzled over the ongoing achievement gap and what to do about it, even as other nations’ school systems at first matched and then surpassed their U.S. peers. Among the 34 market-based, democracy-leaning countries in the Organization for Economic Cooperation and Development (OECD), the United States ranks around 20th annually, earning average or below-average grades in reading, science, and mathematics.

By eighth grade, Harvard economist Roland G. Fryer Jr. noted last year, only 44 percent of American students are proficient in reading and math. The proficiency of African-American students, many of them in underperforming schools, is even lower.

“The position of U.S. black students is truly alarming,” wrote Fryer, the Henry Lee Professor of Economics, who used the OECD rankings as a metaphor for minority standing educationally. “If they were to be considered a country, they would rank just below Mexico in last place.”

Harvard Graduate School of Education (HGSE) Dean James E. Ryan, a former public interest lawyer, says geography has immense power in determining educational opportunity in America. As a scholar, he has studied how policies and the law affect learning, and how conditions are often vastly unequal.

His book “Five Miles Away, A World Apart” (2010) is a case study of the disparity of opportunity in two Richmond, Va., schools, one grimly urban and the other richly suburban. Geography, he says, mirrors achievement levels.

A ZIP code as predictor of success

“Right now, there exists an almost ironclad link between a child’s ZIP code and her chances of success,” said Ryan. “Our education system, traditionally thought of as the chief mechanism to address the opportunity gap, instead too often reflects and entrenches existing societal inequities.”

Urban schools demonstrate the problem. In New York City, for example, only 8 percent of black males graduating from high school in 2014 were prepared for college-level work, according to the CUNY Institute for Education Policy, with Latinos close behind at 11 percent. The preparedness rates for Asians and whites — 48 and 40 percent, respectively — were unimpressive too, but nonetheless were firmly on the other side of the achievement gap.

In some impoverished urban pockets, the racial gap is even larger. In Washington, D.C., 8 percent of black eighth-graders are proficient in math, while 80 percent of their white counterparts are.

Fryer said that in kindergarten black children are already 8 months behind their white peers in learning. By third grade, the gap is bigger, and by eighth grade is larger still.

According to a recent report by the Education Commission of the States, black and Hispanic students in kindergarten through 12th grade perform on a par with the white students who languish in the lowest quartile of achievement.

There was once great faith and hope in America’s school systems. The rise of quality public education a century ago “was probably the best public policy decision Americans have ever made because it simultaneously raised the whole growth rate of the country for most of the 20th century, and it leveled the playing field,” said Robert Putnam, the Peter and Isabel Malkin Professor of Public Policy at HKS, who has written several best-selling books touching on inequality, including “Bowling Alone: The Collapse and Revival of the American Community” and “Our Kids: The American Dream in Crisis.”

Historically, upward mobility in America was characterized by each generation becoming better educated than the previous one, said Harvard economist Lawrence Katz. But that trend, a central tenet of the nation’s success mythology, has slackened, particularly for minorities.

“Thirty years ago, the typical American had two more years of schooling than their parents. Today, we have the most educated group of Americans, but they only have about .4 more years of schooling, so that’s one part of mobility not keeping up in the way we’ve invested in education in the past,” Katz said.

As globalization has transformed and sometimes undercut the American economy, “education is not keeping up,” he said. “There’s continuing growth of demand for more abstract, higher-end skills” that schools aren’t delivering, “and then that feeds into a weakening of institutions like unions and minimum-wage protections.”

“The position of U.S. black students is truly alarming.” — Roland G. Fryer Jr.

Fryer is among a diffuse cohort of Harvard faculty and researchers using academic tools to understand the achievement gap and the many reasons behind problematic schools. His venue is the Education Innovation Laboratory , where he is faculty director.

“We use big data and causal methods,” he said of his approach to the issue.

Fryer, who is African-American, grew up poor in a segregated Florida neighborhood. He argues that outright discrimination has lost its power as a primary driver behind inequality, and uses economics as “a rational forum” for discussing social issues.

Better schools to close the gap

Fryer set out in 2004 to use an economist’s data and statistical tools to answer why black students often do poorly in school compared with whites. His years of research have convinced him that good schools would close the education gap faster and better than addressing any other social factor, including curtailing poverty and violence, and he believes that the quality of kindergarten through grade 12 matters above all.

Supporting his belief is research that says the number of schools achieving excellent student outcomes is a large enough sample to prove that much better performance is possible. Despite the poor performance by many U.S. states, some have shown that strong results are possible on a broad scale. For instance, if Massachusetts were a nation, it would rate among the best-performing countries.

At HGSE, where Ferguson is faculty co-chair as well as director of the Achievement Gap Initiative, many factors are probed. In the past 10 years, Ferguson, who is African-American, has studied every identifiable element contributing to unequal educational outcomes. But lately he is looking hardest at improving children’s earliest years, from infancy to age 3.

In addition to an organization he founded called the Tripod Project , which measures student feedback on learning, he launched the Boston Basics project in August, with support from the Black Philanthropy Fund, Boston’s mayor, and others. The first phase of the outreach campaign, a booklet, videos, and spot ads, starts with advice to parents of children age 3 or younger.

“Maximize love, manage stress” is its mantra and its foundational imperative, followed by concepts such as “talk, sing, and point.” (“Talking,” said Ferguson, “is teaching.”) In early childhood, “The difference in life experiences begins at home.”

At age 1, children score similarly

Fryer and Ferguson agree that the achievement gap starts early. At age 1, white, Asian, black, and Hispanic children score virtually the same in what Ferguson called “skill patterns” that measure cognitive ability among toddlers, including examining objects, exploring purposefully, and “expressive jabbering.” But by age 2, gaps are apparent, with black and Hispanic children scoring lower in expressive vocabulary, listening comprehension, and other indicators of acuity. That suggests educational achievement involves more than just schooling, which typically starts at age 5.

Key factors in the gap, researchers say, include poverty rates (which are three times higher for blacks than for whites), diminished teacher and school quality, unsettled neighborhoods, ineffective parenting, personal trauma, and peer group influence, which only strengthens as children grow older.

“Peer beliefs and values,” said Ferguson, get “trapped in culture” and are compounded by the outsized influence of peers and the “pluralistic ignorance” they spawn. Fryer’s research, for instance, says that the reported stigma of “acting white” among many black students is true. The better they do in school, the fewer friends they have — while for whites who are perceived as smarter, there’s an opposite social effect.

The researchers say that family upbringing matters, in all its crisscrossing influences and complexities, and that often undercuts minority children, who can come from poor or troubled homes. “Unequal outcomes,” he said, “are from, to a large degree, inequality in life experiences.”

Trauma also subverts achievement, whether through family turbulence, street violence, bullying, sexual abuse, or intermittent homelessness. Such factors can lead to behaviors in school that reflect a pervasive form of childhood post-traumatic stress disorder.

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Possible solutions to educational inequality:

• Access to early learning
• Improved K-12 schools
• More family mealtimes
• Reinforced learning at home
• Data-driven instruction
• Longer school days, years
• Respect for school rules
• Small-group tutoring
• High expectations of students
• Safer neighborhoods

[/gz_sidebar]

At Harvard Law School, both the Trauma and Learning Policy Initiative and the Education Law Clinic marshal legal aid resources for parents and children struggling with trauma-induced school expulsions and discipline issues.

At Harvard Business School, Karim R. Lakhani, an associate professor who is a crowdfunding expert and a champion of open-source software, has studied how unequal racial and economic access to technology has worked to widen the achievement gap.

At Harvard’s Project Zero, a nonprofit called the Family Dinner Project is scraping away at the achievement gap from the ground level by pushing for families to gather around the meal table, which traditionally was a lively and comforting artifact of nuclear families, stable wages, close-knit extended families, and culturally shared values.

Lynn Barendsen, the project’s executive director, believes that shared mealtimes improve reading skills, spur better grades and larger vocabularies, and fuel complex conversations. Interactive mealtimes provide a learning experience of their own, she said, along with structure, emotional support, a sense of safety, and family bonding. Even a modest jump in shared mealtimes could boost a child’s academic performance, she said.

“We’re not saying families have to be perfect,” she said, acknowledging dinnertime impediments like full schedules, rudimentary cooking skills, the lure of technology, and the demands of single parenting. “The perfect is the enemy of the good.”

Whether poring over Fryer’s big data or Barendsen’s family dinner project, there is one commonality for Harvard researchers dealing with inequality in education: the issue’s vast complexity. The achievement gap is a creature of interlocking factors that are hard to unpack constructively.

Going wide, starting early

With help from faculty co-chair and Jesse Climenko Professor of Law Charles J. Ogletree, the Achievement Gap Initiative is analyzing the factors that make educational inequality such a complex puzzle: home and family life, school environments, teacher quality, neighborhood conditions, peer interaction, and the fate of “all those wholesome things,” said Ferguson. The latter include working hard in school, showing respect, having nice friends, and following the rules, traits that can be “elements of a 21st-century movement for equality.”

In the end, best practices to create strong schools will matter most, said Fryer.

He called high-quality education “the new civil rights battleground” in a landmark 2010 working paper for the Handbook of Labor Economics called “Racial Inequality in the 21st Century: The Declining Significance of Discrimination.”

Fryer tapped 10 large data sets on children 8 months to 17 years old. He studied charter schools, scouring for standards that worked. He champions longer school days and school years, data-driven instruction, small-group tutoring, high expectations, and a school culture that prizes human capital — all just “a few simple investments,” he wrote in the working paper. “The challenge for the future is to take these examples to scale” across the country.

How long would closing the gap take with a national commitment to do so? A best-practices experiment that Fryer conducted at low-achieving high schools in Houston closed the gap in math skills within three years, and narrowed the reading achievement gap by a third.

“You don’t need Superman for this,” he said, referring to a film about Geoffrey Canada and his Harlem Children’s Zone, just high-quality schools for everyone, to restore 19th-century educator Horace Mann’s vision of public education as society’s “balance-wheel.”

Last spring, Fryer, still only 38, won the John Bates Clark medal, the most prestigious award in economics after the Nobel Prize. He was a MacArthur Fellow in 2011, became a tenured Harvard professor in 2007, was named to the prestigious Society of Fellows at age 25. He had a classically haphazard childhood, but used school to learn, grow, and prosper. Gradually, he developed a passion for social science that could help him answer what was going wrong in black lives because of educational inequality.

With his background and talent, Fryer has a dramatically unique perspective on inequality and achievement, and he has something else: a seemingly counterintuitive sense that these conditions will improve, once bad schools learn to get better. Discussing the likelihood of closing the achievement gap if Americans have the political and organizational will to do so, Fryer said, “I see nothing but optimism.”

Correction: An earlier version of this story inaccurately portrayed details of Dr. Fryer’s background.

Illustration by Kathleen M.G. Howlett. Harvard staff writer Christina Pazzanese contributed to this report.

Next Tuesday: Inequality in health care

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Dependence on Tech Caused ‘Staggering’ Education Inequality, U.N. Agency Says

Heavy reliance on online remote learning during the pandemic drew attention away from more equitable ways of teaching children at home, a UNESCO report says.

By Natasha Singer

Natasha Singer has chronicled the growth of online learning in U.S. public schools for more than a decade.

In early 2020, as the coronavirus spread, schools around the world abruptly halted in-person education. To many governments and parents, moving classes online seemed the obvious stopgap solution.

In the United States, school districts scrambled to secure digital devices for students. Almost overnight, videoconferencing software like Zoom became the main platform teachers used to deliver real-time instruction to students at home.

Now a report from UNESCO , the United Nations’ educational and cultural organization, says that overreliance on remote learning technology during the pandemic led to “staggering” education inequality around the world. It was, according to a 655-page report that UNESCO released on Wednesday, a worldwide “ed-tech tragedy.”

The report, from UNESCO’s Future of Education division, is likely to add fuel to the debate over how governments and local school districts handled pandemic restrictions, and whether it would have been better for some countries to reopen schools for in-person instruction sooner.

The UNESCO researchers argued in the report that “unprecedented” dependence on technology — intended to ensure that children could continue their schooling — worsened disparities and learning loss for hundreds of millions of students around the world, including in Kenya, Brazil, Britain and the United States.

The promotion of remote online learning as the primary solution for pandemic schooling also hindered public discussion of more equitable, lower-tech alternatives, such as regularly providing schoolwork packets for every student, delivering school lessons by radio or television — and reopening schools sooner for in-person classes, the researchers said.

“Available evidence strongly indicates that the bright spots of the ed-tech experiences during the pandemic, while important and deserving of attention, were vastly eclipsed by failure,” the UNESCO report said.

The UNESCO researchers recommended that education officials prioritize in-person instruction with teachers, not online platforms, as the primary driver of student learning. And they encouraged schools to ensure that emerging technologies like A.I. chatbots concretely benefitted students before introducing them for educational use.

Education and industry experts welcomed the report, saying more research on the effects of pandemic learning was needed.

“The report’s conclusion — that societies must be vigilant about the ways digital tools are reshaping education — is incredibly important,” said Paul Lekas, the head of global public policy for the Software & Information Industry Association, a group whose members include Amazon, Apple and Google. “There are lots of lessons that can be learned from how digital education occurred during the pandemic and ways in which to lessen the digital divide. ”

Jean-Claude Brizard, the chief executive of Digital Promise, a nonprofit education group that has received funding from Google, HP and Verizon, acknowledged that “technology is not a cure-all.” But he also said that while school systems were largely unprepared for the pandemic, online education tools helped foster “more individualized, enhanced learning experiences as schools shifted to virtual classrooms.”

​Education International, an umbrella organization for about 380 teachers’ unions and 32 million teachers worldwide, said the UNESCO report underlined the importance of in-person, face-to-face teaching.

“The report tells us definitively what we already know to be true, a place called school matters,” said Haldis Holst, the group’s deputy general secretary. “Education is not transactional nor is it simply content delivery. It is relational. It is social. It is human at its core.”

Here are some of the main findings in the report:

The promise of education technology was overstated.

For more than a decade, Silicon Valley tech giants as well as industry-financed nonprofit groups and think tanks have promoted computers, apps and internet access in public schools as innovations that would quickly democratize and modernize student learning.

Many promised that such digital tools would allow schoolchildren to more easily pursue their interests, learn at their own pace and receive instant automated feedback on their work from learning analytics algorithms.

The report’s findings challenge the view that digital technologies are synonymous with educational equality and progress.

The report said that when coronavirus cases began spiking in early 2020, the overselling of ed-tech tools helped make remote online learning seem like the most appealing and effective solution for pandemic schooling even as more equitable, lower-tech options were available.

Remote online learning worsened education disparities.

UNESCO researchers found the shift to remote online learning tended to provide substantial advantages to children in wealthier households while disadvantaging those in lower-income families.

By May 2020, the report said, 60 percent of national remote learning programs “relied exclusively” on internet-connected platforms. But nearly half a billion young people — about half the primary and secondary students worldwide — targeted by those remote learning programs lacked internet connections at home, the report said, excluding them from participating.

According to data and surveys cited in the report, one-third of kindergarten through 12th-grade students in the United States “were cut off from education” in 2020 because of inadequate internet connections or hardware. In 2021 in Pakistan, 30 percent of households said they were aware of remote learning programs while fewer than half of this group had the technology needed to participate.

Learning was hindered and altered.

Student learning outcomes stalled or “declined dramatically” when schools deployed ed tech as a replacement for in-person instruction, the UNESCO researchers said, even when children had access to digital devices and internet connections.

The report also said students learning online spent considerably less time on formal educational tasks — and more time on monotonous digital tasks. It described a daily learning routine “less of discovery and exploration than traversing file-sharing systems, moving through automated learning content, checking for updates on corporate platforms and enduring long video calls.”

Remote online learning also limited or curtailed student opportunities for socialization and nonacademic activities, the report said, causing many students to become disengaged or drop out of school.

The report warned that the shift to remote learning also gave a handful of tech platforms — like Google and Zoom — extraordinary influence in schools. These digital systems often imposed private business values and agendas, the report added, that were at odds with the “humanistic” values of public schooling.

Regulation and guardrails are needed.

To prevent a repeat scenario, the researchers recommended that schools prioritize the best interests of schoolchildren as the central criteria for deploying ed tech.

In practical terms, the researchers called for more regulation and guardrails around online learning tools. They also suggested that districts give teachers more say over which digital tools schools adopt and how they are used.

Natasha Singer writes about technology, business and society. She is currently reporting on the far-reaching ways that tech companies and their tools are reshaping public schools, higher education and job opportunities. More about Natasha Singer

Unequal Opportunity: Race and Education

Subscribe to governance weekly, linda darling-hammond ld linda darling-hammond.

March 1, 1998

• 13 min read

W.E.B. DuBois was right about the problem of the 21st century. The color line divides us still. In recent years, the most visible evidence of this in the public policy arena has been the persistent attack on affirmative action in higher education and employment. From the perspective of many Americans who believe that the vestiges of discrimination have disappeared, affirmative action now provides an unfair advantage to minorities. From the perspective of others who daily experience the consequences of ongoing discrimination, affirmative action is needed to protect opportunities likely to evaporate if an affirmative obligation to act fairly does not exist. And for Americans of all backgrounds, the allocation of opportunity in a society that is becoming ever more dependent on knowledge and education is a source of great anxiety and concern.

At the center of these debates are interpretations of the gaps in educational achievement between white and non-Asian minority students as measured by standardized test scores. The presumption that guides much of the conversation is that equal opportunity now exists; therefore, continued low levels of achievement on the part of minority students must be a function of genes, culture, or a lack of effort and will (see, for example, Richard Herrnstein and Charles Murray’s The Bell Curve and Stephan and Abigail Thernstrom’s America in Black and White).

The assumptions that undergird this debate miss an important reality: educational outcomes for minority children are much more a function of their unequal access to key educational resources, including skilled teachers and quality curriculum, than they are a function of race. In fact, the U.S. educational system is one of the most unequal in the industrialized world, and students routinely receive dramatically different learning opportunities based on their social status. In contrast to European and Asian nations that fund schools centrally and equally, the wealthiest 10 percent of U.S. school districts spend nearly 10 times more than the poorest 10 percent, and spending ratios of 3 to 1 are common within states. Despite stark differences in funding, teacher quality, curriculum, and class sizes, the prevailing view is that if students do not achieve, it is their own fault. If we are ever to get beyond the problem of the color line, we must confront and address these inequalities.

The Nature of Educational Inequality

Americans often forget that as late as the 1960s most African-American, Latino, and Native American students were educated in wholly segregated schools funded at rates many times lower than those serving whites and were excluded from many higher education institutions entirely. The end of legal segregation followed by efforts to equalize spending since 1970 has made a substantial difference for student achievement. On every major national test, including the National Assessment of Educational Progress, the gap in minority and white students’ test scores narrowed substantially between 1970 and 1990, especially for elementary school students. On the Scholastic Aptitude Test (SAT), the scores of African-American students climbed 54 points between 1976 and 1994, while those of white students remained stable.

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Even so, educational experiences for minority students have continued to be substantially separate and unequal. Two-thirds of minority students still attend schools that are predominantly minority, most of them located in central cities and funded well below those in neighboring suburban districts. Recent analyses of data prepared for school finance cases in Alabama, New Jersey, New York, Louisiana, and Texas have found that on every tangible measure—from qualified teachers to curriculum offerings—schools serving greater numbers of students of color had significantly fewer resources than schools serving mostly white students. As William L. Taylor and Dianne Piche noted in a 1991 report to Congress: Inequitable systems of school finance inflict disproportionate harm on minority and economically disadvantaged students. On an inter-state basis, such students are concentrated in states, primarily in the South, that have the lowest capacities to finance public education. On an intra-state basis, many of the states with the widest disparities in educational expenditures are large industrial states. In these states, many minorities and economically disadvantaged students are located in property-poor urban districts which fare the worst in educational expenditures (or) in rural districts which suffer from fiscal inequity.

Jonathan Kozol s 1991 Savage Inequalities described the striking differences between public schools serving students of color in urban settings and their suburban counterparts, which typically spend twice as much per student for populations with many fewer special needs. Contrast MacKenzie High School in Detroit, where word processing courses are taught without word processors because the school cannot afford them, or East St. Louis Senior High School, whose biology lab has no laboratory tables or usable dissecting kits, with nearby suburban schools where children enjoy a computer hookup to Dow Jones to study stock transactions and science laboratories that rival those in some industries. Or contrast Paterson, New Jersey, which could not afford the qualified teachers needed to offer foreign language courses to most high school students, with Princeton, where foreign languages begin in elementary school.

Even within urban school districts, schools with high concentrations of low-income and minority students receive fewer instructional resources than others. And tracking systems exacerbate these inequalities by segregating many low-income and minority students within schools. In combination, these policies leave minority students with fewer and lower-quality books, curriculum materials, laboratories, and computers; significantly larger class sizes; less qualified and experienced teachers; and less access to high-quality curriculum. Many schools serving low-income and minority students do not even offer the math and science courses needed for college, and they provide lower-quality teaching in the classes they do offer. It all adds up.

What Difference Does it Make?

Since the 1966 Coleman report, Equality of Educational Opportunity, another debate has waged as to whether money makes a difference to educational outcomes. It is certainly possible to spend money ineffectively; however, studies that have developed more sophisticated measures of schooling show how money, properly spent, makes a difference. Over the past 30 years, a large body of research has shown that four factors consistently influence student achievement: all else equal, students perform better if they are educated in smaller schools where they are well known (300 to 500 students is optimal), have smaller class sizes (especially at the elementary level), receive a challenging curriculum, and have more highly qualified teachers.

Minority students are much less likely than white children to have any of these resources. In predominantly minority schools, which most students of color attend, schools are large (on average, more than twice as large as predominantly white schools and reaching 3,000 students or more in most cities); on average, class sizes are 15 percent larger overall (80 percent larger for non-special education classes); curriculum offerings and materials are lower in quality; and teachers are much less qualified in terms of levels of education, certification, and training in the fields they teach. And in integrated schools, as UCLA professor Jeannie Oakes described in the 1980s and Harvard professor Gary Orfield’s research has recently confirmed, most minority students are segregated in lower-track classes with larger class sizes, less qualified teachers, and lower-quality curriculum.

Research shows that teachers’ preparation makes a tremendous difference to children’s learning. In an analysis of 900 Texas school districts, Harvard economist Ronald Ferguson found that teachers’ expertise—as measured by scores on a licensing examination, master’s degrees, and experienc—was the single most important determinant of student achievement, accounting for roughly 40 percent of the measured variance in students’ reading and math achievement gains in grades 1-12. After controlling for socioeconomic status, the large disparities in achievement between black and white students were almost entirely due to differences in the qualifications of their teachers. In combination, differences in teacher expertise and class sizes accounted for as much of the measured variance in achievement as did student and family background (figure 1).

Ferguson and Duke economist Helen Ladd repeated this analysis in Alabama and again found sizable influences of teacher qualifications and smaller class sizes on achievement gains in math and reading. They found that more of the difference between the high- and low-scoring districts was explained by teacher qualifications and class sizes than by poverty, race, and parent education.

Meanwhile, a Tennessee study found that elementary school students who are assigned to ineffective teachers for three years in a row score nearly 50 percentile points lower on achievement tests than those assigned to highly effective teachers over the same period. Strikingly, minority students are about half as likely to be assigned to the most effective teachers and twice as likely to be assigned to the least effective.

Minority students are put at greatest risk by the American tradition of allowing enormous variation in the qualifications of teachers. The National Commission on Teaching and America’s Future found that new teachers hired without meeting certification standards (25 percent of all new teachers) are usually assigned to teach the most disadvantaged students in low-income and high-minority schools, while the most highly educated new teachers are hired largely by wealthier schools (figure 2). Students in poor or predominantly minority schools are much less likely to have teachers who are fully qualified or hold higher-level degrees. In schools with the highest minority enrollments, for example, students have less than a 50 percent chance of getting a math or science teacher with a license and a degree in the field. In 1994, fully one-third of teachers in high-poverty schools taught without a minor in their main field and nearly 70 percent taught without a minor in their secondary teaching field.

Studies of underprepared teachers consistently find that they are less effective with students and that they have difficulty with curriculum development, classroom management, student motivation, and teaching strategies. With little knowledge about how children grow, learn, and develop, or about what to do to support their learning, these teachers are less likely to understand students’ learning styles and differences, to anticipate students’ knowledge and potential difficulties, or to plan and redirect instruction to meet students’ needs. Nor are they likely to see it as their job to do so, often blaming the students if their teaching is not successful.

Teacher expertise and curriculum quality are interrelated, because a challenging curriculum requires an expert teacher. Research has found that both students and teachers are tracked: that is, the most expert teachers teach the most demanding courses to the most advantaged students, while lower-track students assigned to less able teachers receive lower-quality teaching and less demanding material. Assignment to tracks is also related to race: even when grades and test scores are comparable, black students are more likely to be assigned to lower-track, nonacademic classes.

When Opportunity Is More Equal

What happens when students of color do get access to more equal opportunities’ Studies find that curriculum quality and teacher skill make more difference to educational outcomes than the initial test scores or racial backgrounds of students. Analyses of national data from both the High School and Beyond Surveys and the National Educational Longitudinal Surveys have demonstrated that, while there are dramatic differences among students of various racial and ethnic groups in course-taking in such areas as math, science, and foreign language, for students with similar course-taking records, achievement test score differences by race or ethnicity narrow substantially.

Robert Dreeben and colleagues at the University of Chicago conducted a long line of studies documenting both the relationship between educational opportunities and student performance and minority students’ access to those opportunities. In a comparative study of 300 Chicago first graders, for example, Dreeben found that African-American and white students who had comparable instruction achieved comparable levels of reading skill. But he also found that the quality of instruction given African-American students was, on average, much lower than that given white students, thus creating a racial gap in aggregate achievement at the end of first grade. In fact, the highest-ability group in Dreeben’s sample was in a school in a low-income African-American neighborhood. These children, though, learned less during first grade than their white counterparts because their teacher was unable to provide the challenging instruction they deserved.

When schools have radically different teaching forces, the effects can be profound. For example, when Eleanor Armour-Thomas and colleagues compared a group of exceptionally effective elementary schools with a group of low-achieving schools with similar demographic characteristics in New York City, roughly 90 percent of the variance in student reading and mathematics scores at grades 3, 6, and 8 was a function of differences in teacher qualifications. The schools with highly qualified teachers serving large numbers of minority and low-income students performed as well as much more advantaged schools.

Most studies have estimated effects statistically. However, an experiment that randomly assigned seventh grade “at-risk”students to remedial, average, and honors mathematics classes found that the at-risk students who took the honors class offering a pre-algebra curriculum ultimately outperformed all other students of similar backgrounds. Another study compared African-American high school youth randomly placed in public housing in the Chicago suburbs with city-placed peers of equivalent income and initial academic attainment and found that the suburban students, who attended largely white and better-funded schools, were substantially more likely to take challenging courses, perform well academically, graduate on time, attend college, and find good jobs.

What Can Be Done?

This state of affairs is not inevitable. Last year the National Commission on Teaching and America’s Future issued a blueprint for a comprehensive set of policies to ensure a “caring, competent, and qualified teacher for every child,” as well as schools organized to support student success. Twelve states are now working directly with the commission on this agenda, and others are set to join this year. Several pending bills to overhaul the federal Higher Education Act would ensure that highly qualified teachers are recruited and prepared for students in all schools. Federal policymakers can develop incentives, as they have in medicine, to guarantee well-prepared teachers in shortage fields and high-need locations. States can equalize education spending, enforce higher teaching standards, and reduce teacher shortages, as Connecticut, Kentucky, Minnesota, and North Carolina have already done. School districts can reallocate resources from administrative superstructures and special add-on programs to support better-educated teachers who offer a challenging curriculum in smaller schools and classes, as restructured schools as far apart as New York and San Diego have done. These schools, in communities where children are normally written off to lives of poverty, welfare dependency, or incarceration, already produce much higher levels of achievement for students of color, sending more than 90 percent of their students to college. Focusing on what matters most can make a real difference in what children have the opportunity to learn. This, in turn, makes a difference in what communities can accomplish.

An Entitlement to Good Teaching

The common presumption about educational inequality—that it resides primarily in those students who come to school with inadequate capacities to benefit from what the school has to offer—continues to hold wide currency because the extent of inequality in opportunities to learn is largely unknown. We do not currently operate schools on the presumption that students might be entitled to decent teaching and schooling as a matter of course. In fact, some state and local defendants have countered school finance and desegregation cases with assertions that such remedies are not required unless it can be proven that they will produce equal outcomes. Such arguments against equalizing opportunities to learn have made good on DuBois’s prediction that the problem of the 20th century would be the problem of the color line.

But education resources do make a difference, particularly when funds are used to purchase well-qualified teachers and high-quality curriculum and to create personalized learning communities in which children are well known. In all of the current sturm und drang about affirmative action, “special treatment,” and the other high-volatility buzzwords for race and class politics in this nation, I would offer a simple starting point for the next century s efforts: no special programs, just equal educational opportunity.

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Sharp increase in inequality in education in times of the COVID-19-pandemic

Carla Haelermans

1 Research Centre for Education and the Labour Market (ROA), School of Business and Economics, Maastricht University, Maastricht, The Netherlands

2 Initiative for Education Research (NRO), Den Haag, The Netherlands

Roxanne Korthals

3 Inspectorate of Education, Utrecht, The Netherlands

4 Education Lab, School of Business and Economics, Maastricht University, Maastricht, The Netherlands

Madelon Jacobs

Suzanne de leeuw, stan vermeulen, lynn van vugt, tijana prokic-breuer, rolf van der velden, sanne van wetten, inge de wolf, associated data.

Data cannot be shared publicly because they are part of the data at Statistics Netherlands and cannot be exported from the secured virtual environment at Statistics Netherlands. However, all data underlying the results presented are available at Statistics Netherlands and use can be requested by national and international researchers via the Netherlands Cohort Study on Education (NCO) and Statistics Netherlands; data access requests may be sent to ln.keozrednotrohoclaanoitan@ofni . An extensive description of the data and the access procedure can be found in Haelermans, C., T. Huijgen, M. Jacobs, M. Levels, R. van der Velden, L. van Vugt and S. van Wetten (2020). Using Data to Advance Educational Research, Policy and Practice: Design, Content and Research Potential of the Netherlands Cohort Study on Education. European Sociological Review, 36(4), 643-662, https://academic.oup.com/esr/article/36/4/643/5871552?login=true .

The COVID-19-pandemic forced many countries to close schools abruptly in the spring of 2020. These school closures and the subsequent period of distance learning has led to concerns about increasing inequality in education, as children from lower-educated and poorer families have less access to (additional) resources at home. This study analyzes differences in declines in learning gains in primary education in the Netherlands for reading, spelling and math, using rich data on standardized test scores and register data on student and parental background for almost 300,000 unique students. The results show large inequalities in the learning loss based on parental education and parental income, on top of already existing inequalities. The results call for a national focus on interventions specifically targeting vulnerable students.

Introduction

The COVID-19-pandemic of early 2020 interrupted or even completely halted the learning of children in many countries around the world. Globally, schools were closed for an average of almost 95 school days between March 2020 and February 2021 [ 1 ], which is equivalent to almost half a school year in countries where a school year is 40 weeks. In many western countries, schools continued to teach remotely. However, there were many challenges related to distance learning, such as access to digital learning devices and digital learning gaps [e.g., 2 – 4 ]. This prompted serious worries of social-emotional problems and learning loss. Despite the lack of adequate data in many countries, some studies appeared on the use of online learning tools by students [e.g., 5 ] and on the effect of distance schooling on performance and learning gains of students in primary education. Although some studies did not find significant learning losses [e.g., no effects on reading in the USA [ 6 ], no learning deficits on schools with a large share of students with advantaged backgrounds in Australia [ 7 ]], most studies report negative consequences of the school closures for children’s educational development [Belgium [ 2 ], UK [ 4 , 8 ], Italy [ 9 ], Switzerland [ 10 ], Germany [ 11 ], USA [ 12 – 15 ], Norway [ 16 ]]. For higher education, the results are less consistent: some find negative effects [ 17 ] while others indicate that distance learning might have made students more efficient [ 18 ] or see little effects [ 19 ].

There are worries that some groups of students experienced lower learning gains due to the school closures and the COVID-19-pandemic than others. Our hypothesis is that the school closures and the pandemic resulted in increased inequality in skill development for students from specific backgrounds (socio-economic status, income and migration background). There is reason to believe that inequalities have indeed increased due to the school closures. For instance, in the Netherlands especially lower-educated parents felt less capable in helping their children with their schoolwork [ 20 , 21 ]. In the United Kingdom, we see that middle class parents spent more time on home schooling than parents from the working class [ 22 , 23 ]. If this is the case, and these learning losses persist, they can be detrimental for development of skills in the long run, and in turn lead to an increase of the existing inequalities in opportunities in education and on the labor market [ 24 ].

Previous studies on inequalities based on socioeconomic background variables in students’ learning gains during the school closures, were hampered by data limitations. Some were limited by their data on educational performance: they used relatively small samples, focused on a specific region rather than a national representative sample, or were limited to only one grade level or subject [ 6 , 8 , 9 , 12 , 13 , 16 ]. Others had limited information on students’ background characteristics. They used school level indicators [ 2 , 7 , 11 ] or relatively uninformative categories. For example, a recent study based on Dutch data was only able to distinguish between families in which at least one parent had a lower secondary degree (92%) and families in which both parents had less than a lower secondary degree (8%) [ 3 ]. Our study improves upon these studies for several reasons: 1) as a result of the widespread use of standardized testing in the Netherlands, we have a large sample of students who were tested shortly before and after the first lockdown, 2) we have rich student background information at the individual level, including multiple student background variables that indicate whether a student is disadvantaged or not, based on meaningful and informative categories and 3) we focus on effects for separate grade levels, and three different subjects (reading, spelling and mathematics) showing large variation, instead of only looking at overall effects or one subject.

Therefore, in the study at hand, we are able to look in greater detail at background differences between students and present results showing that the learning loss due to the school closures are unequally distributed: students from disadvantaged backgrounds have suffered much more than their fellow students. To show this, we use standardized test score data from the Netherlands and link this to register data on student and parental background for primary school students.

COVID-19 educational policy changes in the Netherlands

Although compulsory education starts at age 5, Dutch children generally enter primary school at age 4. They remain in primary school up to age 12, after which they enter secondary school and are tracked according to their ability. Almost all schools in the Netherlands are public schools (99%) funded by the Ministry of Education, Culture and Science [ 25 ].

February 27, 2020 the first COVID-19 patient was reported in the Netherlands. Primary schools closed at March 16, 2020 and reopened May 11, 2020. Vulnerable children, and children of parents with essential occupations who could not work from home, were allowed to come to school during the school closure. However, these children usually followed the same program as the children who had to stay at home and comprised only around 5% of all children in this first period of school closure. Up to June 7, 2020 children only went to school half of the time. In this way, groups were smaller and it was easier to keep distance. From June 8 onwards schools went back their usual schedule. Children and teachers were still urged to stay at home when they showed any symptoms associated with COVID-19.

The Netherlands was relatively well equipped for online education, as a total of 96% of the Dutch households have internet access at home [ 26 ]. Additionally, the Dutch government made 2.5 million euros available in March to support online learning. This money was used to buy laptops and/or to provide internet access for 7,000 students. This money was supplemented with another 3.8 million euros in May 2020. In total, over 16,000 laptops and tablets were financed in this way [ 27 ]. Nevertheless, the school closure happened relatively sudden with no time to prepare. Teachers had to improvise, students suddenly had to structure their own school day, and parents had to act as teachers for their children. Although we do not know exactly how much education children received while schools were closed, there are strong indications that children spent less time on their education than usual. Studies in Germany and Switzerland report considerable reduction in studying time during school closings [ 28 , 29 ]. Moreover, a survey among Dutch parents revealed that parents, especially in disadvantaged families, did often not feel equipped to support their children during the school closing [ 20 ].

Children in countries with longer school closings and less internet access might have experienced larger learning losses and larger inequalities because they experienced prolonged periods of limited and unequal excess to education. In line with this hypothesis, a recent study in Italy [ 9 ] reports larger learning losses (0.19 SD) than previous studies in the Netherlands (0.08 SD) [ 3 ]. In Italy schools were closed for 15 weeks (one of the first and longest school closings in Europe). Moreover Italy has one of the lowest share of households with a broadband connection [ 30 ] and 12% of the students between 6 and 17 years old did not have access to a computer or digital tools at home in 2018/2019 [ 31 ]. However, contradicting the idea that longer school closings result in larger learning deficits, a study in Belgium—where schools closed for 8.5 weeks—reports a reduction in mathematics scores of 0.19 SD [ 2 ] which is similar in size to the effects found in Italy (15 weeks). Altogether, more research based on country comparisons is needed to be able to state that longer lockdowns result in larger learning deficits and an increase in educational inequalities.

In the Netherlands, students take standardized tests throughout grades 1 to grade 6 in primary education. These standardized tests come from different suppliers, with the largest supplier being CITO, with which we collaborated for this paper. Furthermore, schools use administration systems to store the information about the standardized test scores. Three administration systems exported the data on standardized test scores from school year 2013/2014 onwards as part of the Netherlands Cohort Study on Education (NCO) project, a national project initiated by the Dutch Research Council (for a description of this project, see [ 32 ]). With permission of the schools, the administration system exports the data on the standardized test scores to Statistics Netherlands, who pseudonominize the student-id and school-id. Before any data was exported, parents were informed about the project and data export by the school, and were given the opportunity (during 4 to 6 weeks) to object against export of their child(ren)’s data (by informing the school written or orally). The school registered any objections in their administration system, and data was not exported from those students whose parents objected.

The data was collected over a period of three months with two exports from the administration systems, the first export took place on the 30th of November 2020, the second on the 18th of January 2021. In this export, information is collected from school years 2013/2014 to 2019/2020 and gradually consists of more and more students in more and more grades. For more information, see Table 1 . In total, 1,319 schools and unique information of 291,635 students was gathered on standardized test scores. After cleaning the data, the total sample used for analyses of this paper comes down to 201,819 students in 1,178 schools.

National standardized test scores

From grade 1 to grade 6, students take standardized tests twice a year, a midterm test, most often administered to students in the months January and February of the school year, and an end-of-term test, mostly administered in the months June and July, right before the summer holidays. For most schools, these are digital tests. Some schools opt for the pen-and-paper version. Due to the school closure in the spring of 2020, for the school year 2019/2020 the end-of-term test could be postponed until after the summer holidays, which many schools did: about a quarter of schools decided to test their students after the summer holidays in August, September or even October. Test supplier CITO made a recalculation for the test scores in August, September and October to account for the extra time until the test, and make them comparable to the test scores of students who made the test before summer. Note that the tests written during the pandemic were exactly the same type and format as before the pandemic, and there is no within school variation between the type and format of the tests before and during the pandemic.

We use test scores in the domains reading, spelling and math. Table 2 shows the number of test records and unique students per domain. The test in math contains both abstract problems and contextual problems that describe a concrete task. The reading test assesses the student’s ability to understand written texts, including both factual and literary content. Lastly, the test in spelling asks students to write down a series of words (no verbs), demonstrating that they have learned the spelling rules. For reading, there is no mid-term test in the first grade, therefore the learning gains between the midterm test and end-of-term test cannot be calculated for grade 1.

The learning gains are defined based on the standardized test scores and are calculated by subtracting the score on the midterm test from the end-of-term test of each domain within a school year, with the condition that the student must have taken a midterm and end-of-term test within the same school year at the same school. To remove the influence of outliers, the top and bottom 1% of the absolute learning gains scores are not included in the analyses.

Student background variables

In the secured virtual environment of Statistics Netherlands, standardized test scores can be matched to background information of the students and their parents. Note that the data in the environment of Statistics Netherlands are pseudonymized such that data are fully anonymous to the researchers that use these data. The data on background information that we use are the highest education level and highest income of parents, student migration background and student gender. Parental education is defined as low when the highest obtained degree of (one) the parents is in pre-vocational secondary education (vmbo b/k), or a degree in upper secondary vocational education (mbo 1), or grades 7 to 9 in pre-vocational secondary education (vmbo gl/tl) or senior general secondary education or university preparatory education (1), middle when a degree in upper secondary vocational education level 2, 3 or 4, or when completed senior general secondary education or university preparatory education (2), and high when a degree at a university of applied sciences is attained or higher (3). This division of parental education over three categories is also being used in the Netherlands Cohort Study on Education and leads to a division in categories that is not only relevant at the content level, but also provides us with large enough groups to have statistical power. Highest parental income is defined as low when the highest income of one of the parents is below the minimum income level (1), middle when the income is higher than minimal level but below twice the minimum income level (2) and high when the income of one of the parents is higher than twice the minimum income. Students’ migration background is defined as either having a Dutch background or a western background, or a non-western background. Students with a Dutch or western background are combined into one category because the data contains only very few students with a western background, and the results of these two groups are very comparable. In terms of parental education and household income, students in our sample with a non-western migration background are more likely to come from households with relatively low educated parents (26% compared to 6% for the native Dutch and western migrant student sample) and a relatively low income (45% compared to 16%). Lastly, the gender of the student is defined as male or female.

Representativeness

The data on standardized test scores are only available for schools who gave permission to export the test scores from their administrative system to Statistics Netherlands. As a result, we do not have full population data and consequently selectivity of the sample might play a role. In the schoolyear 2019/2020, we had a total number of 6,174 primary schools in the Netherlands. The 1,178 schools in our sample therefore comprise a proportion of 19% of the total number of schools. Two main sources of selectivity into the sample can be identified. First, the schools that decided to participate in the data collection might not be random. In exchange for sharing the standardized test score, schools received a report on the performance of their school relative to other schools with a comparable student population. We can expect that active schools, which are keen to monitor their progress, are especially interested in the reports and more likely to participate in the data collection project. Second, not every student is tested. Schools tend to exclude students who are absent (e.g., due to illness) or have a very large learning loss. For these students, schools feel a test is not possible or useful. Usually, the number of students per school which are excluded from the standardized tests is relatively small. However in 2020, after the school closed for several weeks, more schools decided to skip the standardized tests for a larger share of the student population. It is reasonable to assume that students with larger learning losses are less often tested. Therefore, it is likely that our data is not representative for the whole population, and additional tests on our sample in comparison to the full population confirm this. Table 3 shows the representativeness of our sample in comparison to the full population (based on the National Cohort Study on Education; [in Dutch abbreviated as NCO] [ 32 ] on student and school background characteristics. Overall, we see that our sample is over-represented in students with a non-western migration background, and students with low parental income. Furthermore, schools in our sample tend to be larger schools located in more urbanized areas.

Note: N of students is based on the number of unique students in the years 2017/2018, 2018/2019 and 2019/2020.

To limit the impact of selectivity and over-representation of certain students and schools, we use inverse probability weights. In calculating the weights, we use population data on all students enrolled in Dutch primary education and calculate the probability to be in our test score dataset separately per academic year, grade, and test subject domain as a function of students’ observable characteristics. These characteristics are parental education, income, migration background, gender, percentage of students with low educated parents at the school, number of students at the school, urbanisation level (based on location of the school), province (based on location of the school) and school denomination.

Descriptive statistics

Table 4 shows the unstandardized learning growth for the three domains for the 2 years before the pandemic and the year of the pandemic separately. It also shows the learning growth split by group of parental education. Table 4 is used to calculate the normal average learning growth per week in the 20 weeks between midterm and end-of-term test, and the deviation from this in the COVID-19-year. For example, if the normal learning growth for reading is 7 (in 20 weeks time), and during the pandemic it’s only 5, the decline in learning growth in weeks is (20-((20/7)*5) = 5.7.

In order to estimate the effect of the COVID-19 related school closure on students’ learning gain, we compare the learning gain between the midterm and the end-of-term test of the COVID-19-exposed cohort (2019/2020) to the learning gain of students from the two previous cohorts using OLS regressions. To account for potential differences in observable characteristics between students of different cohorts, we add controls for student gender, student household income, migration background, and parental educational background. Further, since for some students of the 2019/2020 cohort the end-of-term test was postponed until the start of the next academic year, we add a dummy indicating whether the test was taken at the end of the 2019/2020 academic year or at the beginning of 2020/2021, resulting in the following regression equation, resembling a difference-in-differences design:

Where Δ y ij stands for the difference in achievement between the end-of-term test and the midterm for student i in grade j . T ij is an indicator for the COVID-19 exposed 2019/2020 cohort, X ij is a vector consisting of the aforementioned control variables, and ε ijs is the school-level clustered error term. β is our coefficient of interest, which captures the difference in average learning gain between the COVID-19 exposed 2019/2020 cohort and the average learning gain of the (pooled) preceding two cohorts (2017/2018 and 2018/2019).

Identification of the COVID-19 effect hinges on the assumption that the learning gain of the different cohorts would have followed a similar trend in the absence of the pandemic. While this assumption is fundamentally untestable, we can provide supporting evidence for it by looking at the variability of learning gains for all grades over time. If these trends are stable, we can be reasonably sure that the difference between the 2019/2020 cohort and the previous two cohorts was caused by the impact of the pandemic. The results of these analyses can be found in Figs ​ Figs5 5 – 10 .

In order to estimate the heterogeneous impact of the COVID-19-pandemic along student background characteristics, we add an interaction between the treatment-dummy and the student characteristic of interest to the regression. This results in the following equation:

Where C ij stands for one of the aforementioned student characteristics: gender, parental education, household income, and migration background. The vector of control variables X ij still includes all other student characteristics. As a robustness check, we also present results of analyses where apart from the interaction we do not include any of the other control variables, with similar results (see Tables ​ Tables5 5 – 10 ). Finally, a concern could be raised that some of the student characteristics we observe are capturing similar things. For example, parental education and household income are likely to be strongly correlated. In order to isolate the additional impact of COVID-19 along (for example) household income, we therefore run analyses where we control for the interaction between parental education and the treatment-dummy in addition to the interaction with household income. In addition to household income, we do this for student gender and migration background as well, resulting in the following equation:

The outcome variable, learning gain between the midterm and the end-of-term test, has been standardized within grade using the inverse probability population weighted means and standard deviations. The baseline category for “COVID-19 year” are the pooled students from the 2017/2018 and 2018/2019 school years. The baseline category for household income is “low”. The baseline category for parental education is “low”. Additional controls include student gender, students’ migration background, and a dummy indicating whether students took their end-of-term test at the start of the next, rather than at the end of the current school year. Observations are weighted using entropy weights. Standard errors are clustered at the school level and are omitted for brevity.

* p<0.10; **<0.05; *** p<0.01.

Note: the outcome variable, learning gain between the midterm and the end-of-term test, has been standardized within grade using the inverse probability population weighted means and standard deviations. The baseline category for “COVID-19 year” are the pooled students from the 2017/2018 and 2018/2019 school years. The baseline category for household income is “low”. The baseline category for parental education is “low”. Additional controls include student gender, students’ migration background, an indicator for student grade, and a dummy indicating whether students took their end-of-term test at the start of the next, rather than at the end of the current school year. Standard errors are clustered at the school level and are omitted for brevity.

With E ij standing for the highest level of obtained parental education. As mentioned before, in our main specification we use inverse probability weighting to obtain results representative for the whole Dutch primary school population. As a robustness check, we run the same analyses without employing weights as well as using entropy-balancing weights ensuring covariate balance between the COVID-19 exposed cohort and the control cohorts (similar to the method used by [ 3 ]), and obtain similar results (see Tables ​ Tables5 5 – 10 ).

In this section, we show the consequences for inequality during the COVID-19-pandemic by comparing the learning gains of students in pre-COVID-19 times (school years 2017/2018 and 2018/2019) to the learning gains since the COVID-19-pandemic (school year 2019/2020) by estimating Eqs ( 1 ) through ( 3 ). The results are presented in Figs ​ Figs1 1 – 4 . Fig 1 presents the results of Eq ( 1 ), estimating the overall impact of COVID-19 on student learning gains. Fig 2 shows the result of Eq ( 2 ), estimating the disparate impact along students’ parental education. Figs ​ Figs3 3 and ​ and4 4 shows the results from Eq ( 3 ), estimating the disparate impact along students’ household income and migration background, in addition to the differences along parental education. Tables ​ Tables11 11 – 15 show all the underlying regression results.

Standardized coefficients of learning gains in COVID-19-year 2019/2020 of low-educated (zero line) versus middle- and high-educated.

The outcome variable, learning gain between the midterm and the end-of-term test, has been standardized within grade using the inverse probability population weighted means and standard deviations. The baseline category for “COVID-19 year” are the pooled students from the 2017/2018 and 2018/2019 school years. The baseline category for gender is “boy”. The baseline category for parental education is “low”. Additional controls include student gender, students’ migration background, parental income and a dummy indicating whether students took their end-of-term test at the start of the next, rather than at the end of the current school year. Observations are weighted using entropy weights. Standard errors are clustered at the school level and are omitted for brevity.

Note: the outcome variable, learning gain between the midterm and the end-of-term test, has been standardized within grade using the inverse probability population weighted means and standard deviations. The baseline category for “COVID-19 year” are the pooled students from the 2017/2018 and 2018/2019 school years. The baseline category for gender is “boy”. The baseline category for parental education is “low”. Additional controls include student gender, students’ migration background, parental income and a dummy indicating whether students took their end-of-term test at the start of the next, rather than at the end of the current school year. Observations are weighted using entropy weights. Standard errors are clustered at the school level and are omitted for brevity.

The outcome variable, learning gain between the midterm and the end-of-termtest, has been standardized within grade using the inverse probability population weighted means and standard deviations. The baseline category for “COVID-19 year” are the pooled students from the 2017/2018 and 2018/2019 school years. The baseline category for household income is “low”. The baseline category for parental education is “low”. Additional controls include student gender, students’ migration background, and a dummy indicating whether students took their end-of-term test at the start of the next, rather than at the end of the current school year. Observations are weighted using entropy weights. Standard errors are clustered at the school level and are omitted for brevity.

Fig 1 shows that in all domains, students have a lower learning gains during the COVID-19-pandemic compared to the growth of students in previous cohorts. On average there is 0.14 standard deviations (SD) learning loss in reading, 0.15 SD in spelling and 0.21 SD in math. Looking at the different grades, we see a gradual increase in the learning loss from grade 1 to grade 5 onwards across all domains, with some outliers, like for instance spelling in grade 4. For reading, we see students experience about 0.06 to 0.20 SD learning loss compared to students from previous cohorts. Looking at spelling shows a similar result, where students experience about 0.13 to 0.18 SD learning loss. Math shows the largest deficits in learning with on average 0.13 (grade 1) to 0.33 (grade 5) SD learning loss.

Although most students learned less in 2019/2020 than their peers in previous cohorts, some students show larger learning loss than others, leading to (increasing) inequality between students. We look at four dimensions of inequality: (1) by parental education, (2) by family income, (3) by migration background and (4) by gender.

Fig 2 shows that children with low-educated parents learned less between the midterm and end-of-year test than their peers with high-educated parents, and that the differences are largest in grades 1, 2 and 3, and for spelling and math(note that alternative specifications in which we use four categories of parental education, or in which we use three categories which are not based on parental education, but on the indication (used for funding purposes) whether a child is a regular child, has a disadvantaged background or a very disadvantaged background, yield very similar results and the same conclusions.) The results show, for example, that children of high-educated parents experience about 0.1 SD more learning gains during the year 2019/2020 compared to children of low-educated parents. In other words, the learning loss due to school closings is larger for students of low-educated parents, and inequalities have grown because of this. The differences between students of high- and low-educated parents are statistically significant for spelling and math but not for reading, implying that the role of parental background on educational development during the first school closure due to the COVID-19-pandemic is largest for math and spelling, and less pronounced for reading comprehension. Altogether, these findings show that the existing differences in learning gains based on parental education prior to the COVID-19-pandemic have increased during the spring of 2020 when learning was disrupted. These increased differences based on parental education are not surprising, since students were more dependent on the help their parents could provide with schoolwork during the school closure. This finding is also confirmed by other studies: parents in the Netherlands with lower educational attainment felt less capable to help their children with schoolwork [ 20 , 21 ].

Parental income also plays a role: Fig 3 shows that children from medium and higher income households increase their scores between the midterm and end-of-year test more strongly in the COVID-19-year than their peers from a family with a lower household income, with the largest effects in grades 2 and 3, and for spelling and math. For example, children from medium and high household income experience about 0.05 SD more learning gains than children in low-income households. Note that the relation between income and learning gains is additive to the additional role of parental education during the pandemic. We explicitly take into account the effect of parental education on learning gains and the additional role during the pandemic, and we still see an effect of household income during the school closure. However, it is not surprising that we find an effect of parental income on top of the effect of parental education: parents with higher household income were more likely to afford additional help for their children during their time at home. One study suggested that they provide more private access to additional online learning materials [ 33 ].

We also looked at the role of migration background, again on top of effects of parental education. The results in Fig 4 indicate that, conditional on the effect of parental education, overall, students with a non-western migration background did not perform significantly worse than other students (native and with a western migration background) during the COVID-19-pandemic. We only find a small significant result for math for grade 2 and 3, and for all grades taken together. Note that, if we do not condition on effects of parental education, we do find significant differences for migration background. Hence, overall, we find that the increased inequality during the pandemic is based on parental education and parental income rather than on migration background.

Lastly, we find that there are no significant differences for gender, neither with nor without controlling for the effect of parental education. Girls seem to perform slightly worse on reading and math but the coefficients are small and almost only statistically significant when all grade levels are taken together (see Table 14 ).

Note: the outcome variable, learning gain between the midterm and the end-of-term test, has been standardized within grade using the inverse probability population weighted means and standard deviations. The baseline category for “COVID-19 year” are the pooled students from the 2017/2018 and 2018/2019 school years. The baseline category for migration background is “no migration background”. The baseline category for parental education is “low”. Additional controls include student gender, students’ migration background, parental income and a dummy indicating whether students took their end-of-term test at the start of the next, rather than at the end of the current school year. Observations are weighted using entropy weights. Standard errors are clustered at the school level and are omitted for brevity.

Robustness checks

Our preferred model, used throughout the main text, includes inverse probability weights to obtain results that are representative for the entire Dutch primary school population, and we compare the learning gains between the midterm and the end-of-term test in the COVID-19-year to the gains in the two years prior. Furthermore, in the analyses mapping the disparate impact of COVID-19 along several student characteristics we control for all other background characteristics. These choices could potentially influence our results and their interpretation. Therefore, in this section we show the results of additional analyses where we change the specification of our main model.

To demonstrate how the choice of including inverse probability weights influences the results, Tables ​ Tables5 5 and ​ and6 6 show the results when using entropy-balancing weights and unweighted regressions, respectively. While there are some slight differences in terms of significance levels of certain coefficients, the overall pattern of lower learning gains during the COVID-19-year for students from low-income households and low educated parents, especially in spelling and math, remains similar in magnitude.

The outcome variable, learning gain between the midterm and the end-of-term test, has been standardized within grade using the inverse probability population weighted means and standard deviations. The baseline category for “COVID-19 year” are the pooled students from the 2017/2018 and 2018/2019 school years. The baseline category for household income is “low”. The baseline category for parental education is “low”. Additional controls include student gender, students’ migration background, and a dummy indicating whether students took their end-of-term test at the start of the next, rather than at the end of the current school year. Standard errors are clustered at the school level and are omitted for brevity.

In Table 7 we run our main specification without controlling for student gender and migration background, and the dummy accounting for whether the end-of-term test was taken at the end of the school year of 2019/2020 or at the beginning of the 2020/2021 school year. It could be that the interaction effects found on student household income and parental educational background only hold conditional on these other student characteristics. If so, this complicates the interpretation of our results. Fortunately, the exclusion of these additional control variables does not change the found associations.

The outcome variable, learning gain between the midterm and the end-of-term test, has been standardized within grade using the inverse probability population weighted means and standard deviations. The baseline category for “COVID-19 year” are the pooled students from the 2017/2018 and 2018/2019 school years. The baseline category for household income is “low”. The baseline category for parental education is “low”. Observations are weighted using inverse probability weights. Standard errors are clustered at the school level and are omitted for brevity.

Table 8 shows how the results change when we control for students’ learning gains that they obtained in the previous year. Including prior performance helps in addressing potential differences between cohorts in the trend of their cognitive development. However, the downside of this specification is that we do not observe prior performance for students that are in the first grade (or second grade for the reading domain), and they drop from the analyses as a result. For the other grades, the results are similar to the main specification. Prior learning gains are significantly positively related to later learning gains for all but one subgroup (grade 5 spelling), but its inclusion does not alter the size and significance of the main results.

The outcome variable, learning gain between the midterm and the end-of-term test, has been standardized within grade using the inverse probability population weighted means and standard deviations. The baseline category for “COVID-19 year” are the pooled students from the 2017/2018 and 2018/2019 school years. The baseline category for household income is “low”. The baseline category for parental education is “low”. Additional controls include student gender, students’ migration background, and a dummy indicating whether students took their end-of-term test at the start of the next, rather than at the end of the current school year. Observations are weighted using inverse probability weights. Standard errors are clustered at the school level and are omitted for brevity.

Table 9 adds school fixed effects to the regression. With this we control for the possibility that time-invariant factors at the school level are driving our results. This could be the case, for example, when students are strongly sorted into schools according to their background characteristics. In this case the association between student characteristics and learning gains could be driven by (unobserved) differences between schools that house different kinds of student populations. The results from Table 9 however show that including school fixed effects does not change the patterns of the found associations.

Finally, Table 10 shows the results of our main specification as well as the previously discussed robustness checks for the pooled sample over all grades. This table further demonstrates that while there are some slight differences in terms of significance between specifications when looking at grades separately, the overall picture of the disparate impact of COVID-19 on student learning gains along household income and parental education levels remains strong and is robust to various alternative model specifications.

Trends in learning gain over time

The interpretation of the differences in learning gains between the cohort affected by the COVID-19 induced lockdown and previous cohorts as attributable to the impact of COVID-19 hinges on the assumption that learning gains would have been similar in the absence of the pandemic. While this assumption is untestable, we can provide supporting evidence for it by looking at the variability of learning gains for all grades over time. If these trends are relatively stable, we can be reasonably sure that the difference between the 2019/2020 cohort and the previous cohorts was caused by the impact of the pandemic. Figs ​ Figs5 5 – 7 show the trends in learning gains per grade over time by plotting the (inverse probability population weighted) unstandardized learning gains for all available cohorts in reading, spelling, and math respectively. Because our data does not go back equally far for all grades, the lines are of different length. As noted earlier, we do not have information on grade 1 learning gains for the reading domain, as grade 1 students do not take a midterm test for this domain. For higher grades, we also have fewer available cohorts due to the manner in which data was collected (see also Table 1 ). Hence, for grade 2 we have data going back to academic year 2014/2015, for grade 3 from 2015/2016 onwards, for grade 4 starting in 2016/2017, and grade 5 starting 2017/2018.

The figures clearly show a marked decrease in learning gains between the COVID-19 affected cohort of the school year 2019/2020 relative to the prior cohorts in all domains and for most grades. For the spelling and math domains, learning gains prior to the COVID-19 cohorts were remarkably stable over time across grades 1 through 4. For grade 5, the 2018/2019 cohort had somewhat lower learning gains than the 2017/2018 cohort. However, since these are the only 2 pre-COVID-19 cohorts for which grade 5 data is available, it is unclear whether this represents a somewhat random fluctuation between cohorts, or whether it is part of a longer trend in declining grade 5 learning gain. For reading, the results are less clear. Grades 2 and 3 show a less stable trend over time than the other grades. For our main estimation sample of the 2017 and 2018 cohorts comprising the control group however, the differences in learning gain between these two cohorts is relatively small for all grades.

A different way of showing whether the 2019/2020 COVID-19 affected cohort is somewhat of an outlier in terms of their regular learning gain, is to plot the prior performance of this cohort during the years in which there was no pandemic and compare it to the performance of an earlier cohort. Figs ​ Figs8 8 – 10 show the learning gain in all prior grades of the students who were in grade 5 during the 2019/2020 school year and those who were in grade 5 during the 2018/2019 school year for reading, spelling and math respectively. If the 2019/2020 cohort is relatively similar to the prior cohort, we should expect to see similar levels (and trends) of learning gain for grades 1 through 4. The pandemic occurred during grade 5 for the 2019/2020 cohort, and we should therefore expect lower levels of learning gain in grade 5 for the 2019/2020 cohort compared to the students that were in grade 5 during the 2018/2019 school year.

Looking at the figures, this is indeed what we see for spelling and math. Both cohorts are on remarkably similar learning gain trajectories from grade 1 through grade 4. In grade 5, the 2019/2020 cohort experiences a stronger decline in learning gain, especially in math, than the students of the previous cohort that were unaffected by the pandemic. For reading, the results are again less stable. The 2018/2019 cohort experienced a stronger decline in learning gain in grade 3 relative to the other grades and the 2019/2020 cohort. The overall pattern of decreasing learning gain from grade 2 to grade 3 and increasing learning gain from grade 3 to grade 4 is visible for both cohorts, however, and the grade 5 learning gain of the pandemic-affected 2019/2020 cohort does decrease more strongly than the learning gain of the prior, unaffected cohort.

Conclusions

This study describes the additional inequality in learning gains of primary school students in the Netherlands during 12 weeks of disrupted learning due to the COVID-19-pandemic for three domains: reading, spelling and math. We show large inequalities in the learning loss based on parental education and parental income, on top of already existing inequalities. The additional inequality in learning gains is largest among grades 2 and 3.

These results are quite alarming and indicate an average delay in learning of about 5.5 weeks for reading, and around 3 weeks for spelling and math with larger deficits in the higher grades. Relative to the period between the midterm- and end-of-term tests of around 20 weeks, this is rather a lot. It is to some extent reassuring that in general the decline in learning gains do not take place in the lowest grades in which the foundation for math and language skills are laid [ 34 ]. On the other hand, the decline in learning gains is larger for students from a low socioeconomic background (lower parental education and household income) for spelling an math and these inequalities are higher in the early grades. We see a delay of around 4 weeks for spelling and math for students with low-educated parents. This implies that during the school closure period students with low-educated parents hardly learned anything. However, there are no statistically significant socioeconomic status differences in reading scores. Previous research has shown that the home environment is important for the development of literacy skills and reading motivation [ 35 , 36 ]. In line with this finding some have also suggested that center-based reading interventions might be less effective than mathematics interventions [ 37 , 38 ]. Our finding that reading skills are less affected by the school closure support the idea that the family environment plays an important role in the development of reading skills, also when schools are open. In contrast, the limited increase in socioeconomic inequalities in reading skills is not in line with our expectations. Normally, family environment is an important source of inequality in reading skills [ 36 ] and we would expect inequalities to rise when schools closed and the role of family environment increased. We attribute the additional inequality in learning loss of students in math and spelling based on parental education and income to better resources these students had: Students with higher-educated parents most likely all possessed a laptop, had parents that were able and willing to help with schoolwork and could even afford additional private tutoring if needed.

The results call for national focus on reducing the learning loss of students from lower-educated parents and lower household income. It is worrisome and unfortunately not unlikely that the increased inequalities in learning loss due to the pandemic may lead to long lasting inequalities, deepening the gap in adult outcomes between groups in the population. This very much stresses the need for targeted interventions to reduce the current inequalities in learning loss caused by the pandemic.

This article shows that schools matter, specifically for the most vulnerable groups. Distance learning may prevent part of the damage but cannot compensate for classroom teaching. The policy implications of these findings are therefore twofold: 1) Government budgets that are made available to make up for learning loss should give schools with many students from low-educated parents and low household income a large share of the pie, and 2) in an event of another crisis, or the current COVID-19-pandemic continues, schools should be closed only as a very last resort to avoid further inequalities.

Funding Statement

The authors gratefully acknowledge financial support from The Netherlands Organisation for Health Research and Development (ZonMw, https://www.zonmw.nl/nl/ ) (project 10430 03201 0014). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Grant acquired by CH, RK, TP-B, RvdV, IdW.

Data Availability

• PLoS One. 2022; 17(2): e0261114.

Decision Letter 0

20 Aug 2021

PONE-D-21-21410

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Reviewer #1: Yes

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Reviewer #1: In general, the article looks interesting and gives good and useful information. There are, however, some minor points that must be corrected before publication.

- One of the most important arguments of this article, compared to previous similar publications is:

"we look in greater detail at background differences between students and present results showing that the learning loss due

to the school closures are unequally distributed and that students from disadvantaged backgrounds have suffered much more than their fellow students."

I would like to understand better what is the meaning of "greater detail" since a new publication should offer something different and it should be clearly explained.

- Getting data from 2018 regarding internet access in Dutch households seems to be a source of information that can be updated for sure with more recent data sources.

- Materials and methods: Standardized tests had the same format in the pandemic? Were they online or face-to-face?

I think authors should (if possible) to describe a little bit better the correction CITO made related to the delay in the tests. This fact is very important because authors are analyzing differences in those tests, actually. We must be sure that there is not any influence in that sense.

- Another point that can be improved is the selection of groups when analyzing parents' conditions. It could be done with easy clustering algorithms to check if their groups are correctly separated. Alternatively, a better explanation about why authors selected three groups (instead of four or two, for example) could be adequate.

-Typos: "the school closures and the COVID-1919-pandemic than others"

"paper comes down to 201819 students in 1178"

"This is implies that during the"

Congratulations for your nice work.

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Reviewer #1:  Yes:  G. M. Sacha

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Author response to Decision Letter 0

September 2021

First, let us express our gratitude for your and the reviewers’ constructive comments and remarks. They have helped us a lot in revising and essentially improving the paper. We believe that we have been able to address all of your comments. We hope that you will be satisfied by the respective revisions and replies. Below is a point-by-point response to the main comments that you stressed in the decision email.

Response to main points mentioned by the editor

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

- Thank you for reminding us about this and our apologies that we did not have this before. We have now adjusted the manuscript to the journals style requirements, including the referencing style.

In the Methods section and the online submission form, please provide additional information about the participant records used in your study. Specifically, please ensure that you have discussed whether all data were fully anonymized before you accessed them.

- We have now added the following to the manuscript on page 7:

“Note that the data in the environment of Statistics Netherlands are pseudonymized such that data are fully anonymous to the researchers that use these data. The pseudonymization key is only known to Statistics Netherlands and they provide separate datasets with the same person identifier, such that the data can be matched, but individuals cannot be identified by the researchers.”

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement.

- Thank you for pointing out that we did not do this in the correct way. We have now removed the funding related text from the manuscript. The Funding statement already was and still is complete, so that does not need to be changed.

In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety.

- Our apologies, it seems that something went wrong with the data availability statement. We have adjusted that now. Please let us know should the current data availability statement not be sufficient.

Please review your reference list to ensure that it is complete and correct.

- Thank you. We have now updated our reference list, and made sure that it’s complete and correct.

Response to reviewer 1

First, let us express our gratitude for your positive words, and constructive comments and remarks. They have helped us a lot in revising and essentially improving the paper. We believe that we have been able to address all of your and their comments. We hope that you will be satisfied by the respective revisions and replies.

In general, the article looks interesting and gives good and useful information.

- Thank you.

There are, however, some minor points that must be corrected before publication.

One of the most important arguments of this article, compared to previous similar publications is:

"we look in greater detail at background differences between students and present results showing that the learning loss due to the school closures are unequally distributed and that students from disadvantaged backgrounds have suffered much more than their fellow students."

- Thank you for this comment. We should indeed have been more specific in discussing our contribution to the existing literature with this study. We have now added some additional explanation to the manuscript, and the text on page 3 we have now added the following:

“In comparison with the study that is most similar that also uses Dutch data (Engzell et al. 2020), our study complements and improves the findings for several reasons. 1) We have a larger sample (18% of all Dutch primary schools, instead of 15%), 2) we have much better and richer student background information at the individual level (instead of socio-economic status measured at the neighborhood level, which is very imprecise), 3) we have multiple student background variables that indicate whether a student is disadvantaged or not, and 4) we focus on effects for separate grade levels, showing large variation there, instead of only looking at overall effects.”

Getting data from 2018 regarding internet access in Dutch households seems to be a source of information that can be updated for sure with more recent data sources.

- Thank you for pointing this out. You are right, and we have now replaced the source by the most recently available data, which is from 2020. The share of households with internet access is the same though, so we did not change the percentage in the text.

Materials and methods: Standardized tests had the same format in the pandemic? Were they online or face-to-face?

- Yes indeed, the standardized test during the pandemic were exactly the same as before the pandemic. For most schools, these are digital tests that take place at school. Some schools opt for the pen-and-paper version. But there is there is no within school variation between type and format of the tests before and during the pandemic. We have now also added this information to the manuscript on page 6.

- You are raising a fair point here. Unfortunately, the details of the correction that CITO has applied to these tests are only known to CITO, and not shared with schools or researchers using these data. However, CITO have a long history of excellent expertise in test development, calibration and correction, so we trust in CITOs many years of experience in these matters.

Having said that, we did include a dummy in all our analyses whether a student participated in a test before or after the summer break, and this does not influence our results. Furthermore, we have also checked whether we find similar results when we only focus on the students that wrote the test before the summer break (so in the regular time frame of the test), and we do not find different results or draw different conclusions. Therefore, we are not worried that the corrected test influences the results in any way.

Another point that can be improved is the selection of groups when analyzing parents' conditions. It could be done with easy clustering algorithms to check if their groups are correctly separated. Alternatively, a better explanation about why authors selected three groups (instead of four or two, for example) could be adequate.

- Thank you for pointing out that we should have explained this in a better way. We have now done that in both the description of the student background variables, and in the description of the results.

On page 7 we have added the following sentence:

“This division of parental education over three categories is also being used in the Netherlands Cohort Study on Education and leads to a division in categories that is not only relevant at the content level, but also provides us with large enough groups to have statistical power.”

Furthermore, on page 15 we have added the following sentence to the results:

“(Note that alternative specifications in which we use four categories of parental education (in a similar way as the Dutch Inspectorate of Education), or in which we use three categories which are not based on parental education, but on the indication (used for funding purposes) whether a child is a regular child, has a disadvantaged background or a very disadvantaged background, yield very similar results and the same conclusions.)”

Typos: "the school closures and the COVID-1919-pandemic than others"

- Thank you, we have corrected the typos in the text and checked once more for other typos as well.

- Thank you, and thank you once more for your valuable comments. We hope we have been able to deal with your comments in a satisfactory way.

Submitted filename: Response to reviewers.docx

Decision Letter 1

19 Oct 2021

PONE-D-21-21410R1Sharp increase in inequality in education in times of the COVID-19-pandemicPLOS ONE

Dear Dr. Haelermans, Let me first apologize for the delay in processing your manuscript on behalf of PLOS ONE. I took over the editorial duty and, because I always aim to obtain  two expert opinions on every manuscript, decided to ask for an additional expertise. You will find the review at the bottom of this email. As you will see, this second reviewer is quite positive about your study but asks for some clarifications. I concur with the reviewer that your study is timely, interesting and relevant and I would encourage you to address the reviewer's comment.

Please submit your revised manuscript by Dec 03 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at  gro.solp@enosolp . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

Jérôme Prado

Reviewer #2: This an interesting and very timely study on pertinent issues in education policy. Below are some concerns I have about the study. Clarifying those would be helpful.

Major Comments:

1. How does the sample used in the study compare to the overall school population in the Netherlands? In other words, how representative is the sample of Netherlands nationally? The authors refer to using a national sample in calculating weights. How do averages of various variables in the study sample compare to those in the national sample?

2. Relatedly, what proportion of total schools in Netherlands does the sample of schools, students and test records in Table 2 represent?

3. On page 6 authors mention that “Test supplier CITO made a recalculation for all test scores to correct for this delayed testing.” Is CITO the only supplier that did this recalculation or did other mentioned suppliers did it too? What kind of impact can we expect this calculation to have on the scores, especially in comparison to other suppliers and to previous years scores? While authors cannot get to the actual calculation CITO did, some explanation would be helpful.

4. Do the authors have any information on teachers or school level variables? Does the study account for teacher and school level effects in their estimations? For example we can expect some teachers to do better in remote instruction that others. Are/Can the estimations accounting for this in any way?

5. Parental Education: They mention on page 7 that “Parental education is defined as low

when the highest obtained degree of (one) the parents is in pre-vocati…..”. They mention further down in the text that they use the higher of the two parents educational attainment. Would it not be pertinent instead to also look at this my mother/father or by the parent with the larger share of child responsibilities? Perhaps, if we expect the parent with lower educational attainment to be more responsible for child caring, look at estimates along that margin?

6. The school closure periods mentioned on page 4 is not very long. How do authors see this in light of closure in other parts of the world e.g. U.S. where schools remained closed for extended periods of time? Can we expect more widening of gaps across different groups if closure remained longer?

7. During the time period of the closure, the vulnerable kids were apparently still allowed to attend schools. How does this, if at all, interact with the income and education of the households. Are authors able to identify students who continued attending during the closure?

8. Results: Education literature generally finds that educational interventions bring a larger change in math and a smaller change in reading scores, partly because reading is not just dependent on what is taught in school but requires stronger input from home also. It would be nice to tie in that literature with the Math vs. Reading losses the authors estimates

9. How should we see migrants in terms of income and education? In other words what is the average education level of migrants in Netherlands and what income category should we expect them to fall into. In other words, more clarity on needed for the reader on analysis over migration vs. income or education.

10. It is not clear what Table 3 is showing. Is it the number of test score observations? If yes, why do we have decimals? If no, then are these some averages of test scores ?

11. Figure 5, 6 and 7 need to be clearer. I could not understand what was being shown by each line.

Minor Comments

1. Relevant study to cite on the effect of pandemic on student learning : https://gpl.gsu.edu/download/student-achievement-growth-during-the-covid-19-pandemic-report-appendix/?wpdmdl=2101&refresh=614b7200638111632334336

2. Contribution: I think in terms of contribution the authors need to think beyond the Dutch data. They emphasize on page 3 the comparison to other work that uses Dutch data. I would urge them to look at other studies, in different countries, that look at inequality in education outcomes during the pandemic and situate their study in the wider literature.

3. The authors mention on page 3 their contribution compared to an existing study on Dutch data. I don’t think having an 18% vs 15% sample is a contribution, unless the new sample is more representative for some reason. The other points about having better and richer background info is certainly something to point out.

Page 25: “figs” should be replaced by Figures

Page 4 : “ At total of 96% of Dutch households “have” internet acces…”

Page 2: For higher education the results are less consistent: some find negative effects [6] while others indicate that distance learning might have made students more efficient [7] or see little effects [8].

Author response to Decision Letter 1

15 Nov 2021

November 2021

Response to reviewer 2

First, let us express our gratitude for your positive words, and constructive and insightful comments and remarks. They have helped us a lot in revising and essentially improving the paper. We believe that we have been able to address all of your and their comments. Based on the comments we extended our discussion of the international literature on the role of the COVID-19 pandemic in children’s educational development, we performed additional robustness checks (e.g., school fixed effects) and clarified some of our tables and figures. We address the comments of the reviewer hereafter in more detail in a point-by-point response. We hope that you will be satisfied by the respective revisions and replies.

This an interesting and very timely study on pertinent issues in education policy.

Below are some concerns I have about the study. Clarifying those would be helpful.

Schools are only included in our sample if they gave permission to share their test scores with Statistics Netherlands. As a result, our sample is not an exact representation of the population of Dutch primary school students. We added Table 3 to the manuscript to show how our study sample compares to the full population. Overall, our sample contains an overrepresentation of one-parent households (19.00% versus 16.16% in the full population), students with a non-western migration background (20.82% versus 14.36%), students with low parental income (24.06% versus 21.20%), and an underrepresentation of students from which both parents work (68.47% versus 70.91%). Furthermore, schools in our sample tend to be larger schools located in more urbanized areas. As you mention already, we use inverse probability weights to minimize the impact of the selectivity of our sample.

Table 3. Representativeness of sample compared to full population on student and school background variables

Full population Sample

Variables Percentage Percentage

Female 49.32 49.75

Migration background

Dutch & western migration background 82.22 75.95

Non-western migration background 17.51 24.04

Missing 0.27 0.01

Parental income

Low income 21.20 24.06

Medium income 53.38 50.54

High income 24.02 24.53

Missing 1.41 0.87

Parental education

Low educated 10.06 11.50

Medium educated 29.87 29.03

High educated 47.59 48.95

Missing 12.48 10.52

School size

Less than 141 students 36.71 28.95

Between 141 – 220 students 30.27 31.26

More than 220 students 33.01 39.79

School level pct of low educated parents

Below 5,5% 33.12 32.22

Between 5,5% and 12% 33.47 32.78

Above 12% 33.41 35.00

Urbanisation level

Low (< 500 adresses/km2) 7.86 5.63

Limited (500 – 1000 adresses/km2) 21.87 14.41

Medium (1000 – 1500 adresses/km2 17.58 12.50

Strong (1500 – 2500 adresses/km2) 30.93 32.66

Very strong (>=2500 adresses/km2) 21.77 34.81

Denomination

Public school 29.79 30.55

Schools based on philosophies 6.10 3.77

Schools based on religious beliefs 64.02 65.69

Observations

Total number 2,458,376 263,553

Note: N of students is based on the number of unique students in the years 2017/2018, 2018/2019 and 2019/2020

Thank you for pointing out that this was not clear. In the schoolyear 2019/2020, we had a total number of 6174 primary schools in the Netherlands. The 1178 schools in our sample therefore comprise a proportion of 19% of the total number of schools.

Having said that, we realised that the distinction between test supplier and administrative system could be confusing (see your next question and our answer). Moreover, the distinction between the different administration systems is not relevant for our analyses. Therefore, we decided to no longer mention the names of the administration systems in our paper and to remove Table 2.

Again: thank you for making us realise this was unclear. Based on your question we realised that there might be confusion between what a test supplier is and what an administrative system is. In the previous version of the paper, we made a distinction between test suppliers and administrative systems. All children in our sample made tests of a supplier called CITO. CITO is by far the largest test supplier in the Netherlands. The test scores of the CITO-tests are saved in an administration system. Schools can choose themselves which administration system they prefer. Our dataset is based on the data stored in three types of administration systems: CITO-LOVS, ParnasSys and ESIS. Former table 2 showed the number of schools per administration system.

However, based on your comment, we realised that the names CITO-LOVS (administration system) and CITO (test supplier) could confuse our readers. As explained in our answer to your previous question, we have now removed Table 2.

So as an answer to your questions 3: there is only one test supplier that we include in our analyses, namely CITO. Therefore, the scores of all students in our sample who performed the test after the summer were corrected. By correcting for the delay, the test supplier aimed to make the test scores comparable to the test scores of students who took the test before summer, to account for the extra time the students had until the test was taken. Note that the test supplier did not correct for the fact that this test was taken in the period of COVID-19 in any way, meaning that they did not correct for lower scores compared to previous years. To make sure our results are not biased by the differences in the timing of the test we also include a dummy in our regression analyses indicating whether a test was made before (=0) or after (=1) summer. Based on your comment, we improved our explanation of the differences in test scores before and after the summer on page 6.

4. Do the authors have any information on teachers or school level variables? Does the study account for teacher and school level effects in their estimations? For example we can expect some teachers to do better in remote instruction that others. Are/Can the estimations account for this in any way?

Thank you for this question. We do have information on school level variables, but unfortunately we do not have any information on the teachers. We agree that school and teacher level factors play a role in the extent to which the COVID-19 related school closings affected student learning. The association between student characteristics and learning gains might be driven by, for example, differences between schools serving different types of student populations. To account for this possibility, we have added a new analysis incorporating Fixed Effects at the school level. These can be found in Table 9 (split by grade level) on page 24 and Table 10 (pooled over all grades) on page 25. In this specification, we account for any (un)observed time invariant differences between schools that could have an effect on the strength of the relationship between student characteristics and the COVID-19 related reductions in learning gains. The results show that the estimations remain unaffected. The possibility that different types of teachers may differentially affect student learning gains unfortunately cannot be tested, as we do not have information on the teachers to whom students have been assigned.

Thank you for this very relevant question. However, we believe that in our setup, parents’ highest obtained level of education is still the best proxy for children’s socioeconomic background for several reasons. One is that we cannot be certain as to which parent has the larger share of child responsibilities. Especially during the time of the pandemic and the increase in remote working, the balance between work and family life is likely to have shifted compared to previous years. This might render interpretation of the results difficult. Secondly, the administrative data on parents’ highest obtained level of education does not yet fully cover the entire population of the Netherlands. In combining the education data from both mother and father, the share of missing observations is reduced. Due to assortative mating there is a relatively high correlation between the highest obtained level of education of both mother and father. Hence, the education of one of the parents is also an indicator of the educational attainment of the other parent.

Nevertheless, you are right that it is instructive to look at the sensitivity of our results to running estimations including either the mother’s or father’s education, or both at the same time, instead of the highest level. The results of this exercise show that pooled over all grades, the main association between parental education and learning loss during the pandemic holds for all three subjects. For reading, maternal education enters more significantly than paternal education, while for the other subjects there is no difference. In the analyses by grade, we again see no differences for spelling and math. For reading, high paternal education appears to be more significantly related to learning losses in grades 3, 4, and 5. However, these results do not survive our standard robustness specifications. Despite the small differences in results based on paternal and maternal education and children’s reading, our overall conclusions do not change when paternal education or maternal education is used. Because of these results, as well as the aforementioned conceptual reasons, we strongly favour the models in which the highest level of parental education is used.

We agree with you that children in countries with longer school closings and less internet access might have experienced larger learning losses and larger inequalities because they experienced prolonged periods of limited and unequal excess to education. However, the current literature is inconclusive and more research is needed to draw pertinent conclusions on the association between the length of the lockdown and children’s educational progress. For example, a recent study in Italy by Contini et al (2021) reports larger learning losses (0.19 SD) than previous studies in the Netherlands (0.08 SD) by Engzell et al (2021). In Italy schools were closed for 15 weeks (one of the first and longest school closings in Europe). Moreover, Italy has one of the lowest share of households with a broadband connection and 12% of the students between 6 and 17 years old did not have access to a computer or digital tools at home in 2018/2019. However, contradicting the idea that longer school closings result in larger learning deficits, a study in Belgium (8.5 weeks of school closing) reports a reduction in mathematics scores of 0.19 SD which is similar in size to the effects found in Italy (15 weeks). Altogether, more research based on country comparisons is needed to be able to state that longer lockdowns result in larger learning deficits and an increase in educational inequalities. We have added a paragraph to reflect on country differences on page 4 and 5.

Thank you for this question. When the Dutch government decided to close schools, some children still went to school. The government prescribed that schools could allow ‘vulnerable’ children as well as children of parents with essential occupations at school. These rules on which children could and could not attend school were purposely vague and schools could mostly define their own policies. As a result, we do not know which children went to school and which children stayed at home. However, especially during the first lockdown, which is the subject of our study, schools were only open for emergencies and the number of children at school was very low. Based on a survey by the General Association of School Leaders about 5% of the students went to school during the first lockdown. Moreover, children who went to school during the school closings usually followed a program which was similar to the program of the children who stayed at home. We have now also added a few sentences on this on page 4. Finally, due to the various reasons to let children spend their day at school, the group of children at school was diverse and exceeded low socioeconomic status families. Since the number of children that attended school is not registered in our data, we cannot make a distinction between children who attended school and who did not attend school during the school closure so we cannot examine the differences in performances based on school attendance.

Whether the presence of a small group of children at school is problematic for the analyses depends on your goal. We argue that we estimate the effect of the Dutch educational policies during the pandemic on children’s education progress. Allowing a selective group of children at school is part of the educational policies and therefore not problematic for our analyses. Nevertheless, we acknowledge that school attendance by vulnerable groups might cause a bias if one wants to estimate the effect of distance education on educational progress. If a bias would occur, one might argue that the results reported in our studies underestimate the inequalities based on parents’ education and income since some inequalities might have been cushioned by school attendance. However, we argue that the bias would be small since the number of children allowed at school was small and the group of children relatively diverse. Moreover, the educational program of children at school and children at home was relatively similar.

Thank you for the helpful literature suggestion. The idea that family environment plays an important role in the development of reading skills is supported by the finding that reading skills are less affected by the school closure. The relative importance of schools might be smaller for reading than for other subjects which explains the limited impact of the pandemic. In contrast, the limited increase in socioeconomic inequalities in reading skills is not in line with our expectations. Normally, family environment is an important source of inequality in reading skills and we would expect inequalities to rise when schools closed and the role of family environment increased. We have added a discussion on the differences between reading and mathematics before and during the pandemic in the Conclusions on page 32 and 33.

You are right that we should clarify to the international audience how students with a non-western migrant background should be seen in terms of parental education and household income. We have added the comparison of the share of low educated parents and low household income between students with a non-western migration background and native / western migrant students on page 8. The share of students with low educated parents and a low household income is higher for those with a non-western migration background than for those with a native / western migration background (26% vs. 6% for low parental education, 45% vs. 16% for low household income.

10. It is not clear what Table 3 is showing. Is it the number of test score observations? If yes, why do we have decimals? If no, then are these some averages of test scores?

In former Table 3 (Table 2 in the new revised version of the paper) we indeed show the number of observations (test records) per domain and per grade. We accidently used the European way to write this down. In continental Europe the meaning of punctuation marks is the exact opposite from Anglo-Saxon countries. In the Netherlands, and many other European countries, dots are used to separate large numbers while commas are used as decimal markers. We revised our punctuation across the manuscript to the Anglo-Saxon manners with commas to separate large numbers and dots as decimal markers. Hopefully this also clarifies the interpretation of Table 2.

Thank you for pointing this out. We have first of all made these figures a bit bigger in the manuscript, such that they are better to read. Furthermore, as for explanation what information the figures show: Figures 5, 6 and 7 show the trends in learning gains per grade over time by plotting the unstandardized learning gains by cohort. Because our data does not go back equally far for all grades, the lines are of different length, which perhaps causes some confusion (see also table 1 on page 5). For grade 5, we have data for the grade 5 cohorts of 2017/2018 until 2019/2020, while for grade 2 we have data for students that were in grade 2 from the 2014/2015 academic year onwards. The main message from these figures is that we see a clear drop in learning gains for most grades during the 2019/2020 academic year for the three domains reading, spelling, and math. We have added some additional clarifying sentences regarding these figures on page 27.

Thank you for the useful suggestion. We included the report in our discussion of the literature on page 3.

We have improved the discussion of previous literature in the introduction (page 2 and 3). The new version of the introduction includes more references to the international literature and has been complemented with recent studies which appeared during the review process of our paper.

In our enthusiasm to show the reader that we have a richer and better dataset to examine the consequences of the school closings on educational growth of primary school students in the Netherlands, we argued that our sample is also somewhat larger than the sample used by Engzell et al. Naturally, this is not the strongest argument. As the reviewer already states, our main improvement is based on the rich background information we have in our data. Based on this comment and the previous comment (minor 2) we have rewritten the paragraph on page 3. We now situate our study in the international literature. We emphasize now how the Dutch context offers, due to standardized testing, unique opportunities to study the consequences of the school closings. Moreover, we emphasize that we have, compared to previous studies in the Netherlands, better information on students’ background characteristics.

Thank you for pointing them out. We have now corrected the typos.

In addition to your comments, we have also substantially shortened the results section that contained a lot of repetition, and have removed some more typographical errors from the paper.

Thank you once more for your valuable comments. We hope we have been able to deal with your comments in a satisfactory way.

Submitted filename: Response to reviewers_revision2.docx

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Attacks on Diversity in Higher Education Threaten Democracy

The forced resignation of Harvard’s president provides a peek at the blueprint for the war against justice in the U.S., concludes a long-time observer of attacks on academia

By Abby L. Ferber

Claudine Gay, president of Harvard University, during a House Education and the Workforce Committee hearing in Washington, D.C., in Dec. 2023.

Haiyun Jiang/Bloomberg via Getty Images

If a time traveler left the U.S. in the summer of 2020, and returned today, they well might conclude they had accidentally gone back in time, so drastically different is today’s national conversation about racism. The murder of George Floyd and the increased visibility of the Black Lives Matter movement had led to corporate and university commitments to “diversity, equity and inclusion.” More white people suddenly publicly recognized white privilege and structural racism. More took to the streets than previous Civil Rights demonstrations. More read and studied systemic racism and white privilege, while learning from diverse writers and educators. More corporations issued statements against racism and pledged to do better.

This racial reckoning threatened the billionaires , politicians and activists intent on protecting extreme free market capitalism and their own economic and political dominance. We can trace December’s House Education Committee hearings—attacking diversity on campuses nationwide—back to those moments three years ago. Three university presidents, all women including Harvard University’s Claudine Gay, a woman of color, were viciously grilled by Republican lawmakers about antisemitism on campus in response to the current war in Gaza. Christopher Rufo, widely recognized as one of the masterminds behind these attacks and many others, proudly proclaimed their real purpose “to eliminate the DEI bureaucracy in every institution in America.” Could this explain why we haven’t witnessed congressional outrage over hate crimes targeting African Americans, the most highly victimized group on college campuses for years?

These hearings, which triggered Gay’s resignation, were meant to have a chilling effect. Not long after, the President of Colorado College, L. Song Richardson, also a woman of color and advocate of justice, resigned , feeling that she could no longer engage in these “deeply challenging conversations,” as a college president. Eliminating the “DEI bureaucracy” is about silencing the voices of those only recently allowed to enter the dialogue and censoring any discussion of inequality. The reality that racism and other forms of inequality even exist is being challenged.

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As a sociologist who who has studied white supremacy for decades, I am scared. It should scare you too. As I warned in my first book White Man Falling: Race, Gender and White Supremacy , mainstream and far right views fall along a continuum. Ideas once considered extreme have now engulfed the Republican party as part of a more ambitious goal of controlling the judicial, executive, and legislative branches of our government. And they are succeeding. Anyone committed to the dream of democracy must act now.

The antisemitism hearings followed years of attacks on DEI, expanding the range of educators, classes, programs, offices, and universities that could be targeted, forcefully reinforced at The Subcommittee on Higher Education and Workforce Development’s Hearing on March 7, "Divisive, Excessive, Ineffective: The Real Impact of DEI on College Campuses" . Even funding for mental health is being denied, at a time when youth and college suicide rates are spiking and are much higher for students of color than white college students on the grounds that this care ”is priming students to learn critical race theory.” It is outrageous to see the politicization of teaching students to work “well with others .” What’s next? Will asking kindergarten students to use their “inside voices” be considered an attack on free speech?!

Fueling these attacks is an overarching myth of liberal indoctrination on campus, seen most clearly in a self-published 2023 report from a widely cited major right-wing think tank. The unscientific Manhattan Institute study would never hold up under peer review by an academic journal. For example, 18- to 20-year-old adults surveyed in the study who said they heard statements like, “white people have white privilege” from any adult in school is presented as proof of indoctrination. Even if we pretend that overhearing this observation represents indoctrination, at best this is a correlation, not to be confused with causality. One of the report’s farfetched and overtly racist conclusions is that black students are hurt by DEI because they will not learn from white students who no longer feel comfortable criticizing them. Personal criticism, not white people critiquing their work . This is their defense of harmful, racist microaggressions . Maybe that’s why discussions of microaggressions are also being banned!

Faulty research like this is being offered as proof that censorship is justified. As of November 2023, legislators have enacted 40 educational gag orders in 22 states, with scores more on the table. The Manhattan Institute even provides model legislation to make it simple. It is dangerously ironic that protecting white, as well as heterosexual and cisgender, students from feeling uncomfortable—dare we call them “snowflakes”—has led to actual, legal censorship, somehow conceived of as free speech.

These legal mandates build upon decades of attempts to censor faculty and curriculum via cyber-attacks and harassment , abuse and threats of violence . The harassment usually begins with an article in one of a handful of student “news” websites like Campus Reform , a program of The Leadership Institute, an organization that trains students to become the next generation of “ conservative activists and leaders in the public policy process .” These sites have published thousands of pieces accusing faculty of liberal indoctrination. They pay students to find stories about “liberal” professors or curtailment of conservative students’ free speech. Many of the incidents are instigated by financially motivated students and are frequently inaccurate or taken out of context and misconstrued . These stories get picked up by other right wing media outlets, sometimes landing on Fox News. Social media trolls then coordinate cyber attacks, which can result in hundreds of venomous emails and social media posts, as well as doxing, many carried out robotically. They have had had devastating consequences for many victims, while foddering claims of liberal indoctrination on campus.

The student organizations behind the websites are given little attention, but the fact that The Leadership Institute ‘s 2022 net assets exceeded $33 million should raise eyebrows. These organizations do not disclose their donors and are enmeshed in a maze of dark money funded by plutocrats desperate to maintain elite white male power in the U.S.

Institutions of higher education should follow the right-wing playbook to collaborate and become proactive. We should take advantage of the excellent resources provided by organizations like the AAUP , PEN America and the American Council on Education to become proactive, support faculty , and move out of the cycle of reaction. I believe these attacks will only worsen. We also need to recognize that these attacks are not new. This is one moment in the long history of politically motivated injustice in education. The majority of Southern states once had laws against teaching enslaved people to read or write, and free Blacks were denied public education in northern and western ones. Schools were legally segregated in this country in living memory. People of color and women were barred from white institutions of higher education and professional degree programs.

We must recognize that education is inherently political . Who can teach, who can learn, and what gets taught are ultimately questions about whose lives matter. The fact that we are still fighting over these questions is all the proof we need that racism still exists.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American, nor the University of Colorado Springs.

Choice, Information Inequity, and the Production, Legitimation, and Reduction of Educational Inequality

Choice is a key part of the culture of the United States. Americans believe deeply in the personal and social usefulness of being able to make many choices. Hence, all sorts of efforts have been made to increase students’ options, whether by creating many different kinds of schools and colleges, offering a great array of majors and degree programs, or allowing multiple modes of attending higher education. However, this proliferation of choices reproduces social inequality in two crucial ways. First, the provision of many options produces social inequality: People often make choices that do not serve their interests as well as they might wish, particularly if they are faced with many options and do not have adequate information. Second, the provision of many choices legitimates social inequality: The more one thinks in terms of choices in the context of a highly individualistic culture such as that of the United States, the easier it is for dominant groups to blame non-dominant groups for creating their own troubles through feckless choices.

This journal article focuses on one particularly important realm of choice—higher education—because it has come to play a central role in the transmission and legitimation of social inequality. Four higher education choices are of particular interest: whether to enter higher education, which college to attend, what major to choose, and what modality to attend college (for example, part time versus full time or in person versus online). Analyzing this choice-making process, the article focuses on the impact of inequitable access to high-quality information. Beyond analyzing how choice proliferation and information inequity join to produce and legitimate educational inequality, the article lays out detailed recommendations for what can be done to reduce this inegalitarian impact.

This article is based on a CCRC working paper, Choice Is Not Always Good: Reducing the Role of Informational Inequality in Producing and Legitimating Higher Education Inequality .

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Q&A: How inclusion in education drives UNESCO laureate to close the gender gap in tech

{reprograma} was awarded the UNESCO Prize for Girls’ and Women’s Education Laureate in 2021 for its commitment to closing the gender gap in Brazil’s technology sector through the provision of training to thousands of under-represented and low-income women and girls in coding and other in-demand digital skills. {reprograma} was showcased during the  Salamanca anniversary celebrations at UNESCO mid-March. 

Barabara spoke to UNESCO about the principal of inclusion in and through education and how it guides the design and implementation of {reprograma}’s curriculum and programmes.

Tell us about yourself and your role at {reprograma}?

As the Diversity, Equity, and Inclusion Manager at   {reprograma}, my role is to understand what women who wish to study with us need be successful in our programmes. For us, this means that we want to see our students empowered with the confidence and skills to access places in our society where they are currently underrepresented or marginalized and to take full advantage of the opportunities that this provides.

This role is important to me as I come from a minority group in Brazil that historically has had less access to opportunities. My mother came to São Paulo as a teenager to work as a nanny and domestic worker and I grew up in disadvantaged neighbourhoods on the outskirts of the city. In my community, affirmative actions and policies were vital to empowering us to continue our education and change our reality. Coming from this background has always made me think about what needs to change in society so that everybody has equal access to all spaces in society, be it in education, in work, in policy-making to name a few.

Can you tell us more about what inclusion in and through education means to you and how you are achieving it at {reprograma}?

Inclusion means ensuring that everyone, regardless of income, race, gender, age, geographic location, etc., has equal access and opportunity to fully participate in all aspects of our society. While this is not the reality of the world we live in today, I am convinced that education is one of the most powerful tools to achieve this goal. 

I am particularly proud of {reprograma}’s inclusive selection process that has been designed to ensure we give preferential entry to women, including black and transgender women, who would otherwise not have the opportunity or means to access IT courses. 

Other inclusive initiatives that we have incorporated into our programmes include ensuring that our in-person events and courses are accessible to mothers. We also have a person who is dedicated to providing individualized support to students who need assistance with the technical content during their studies with us.

How is {reprograma} collaborating with the private sector and other organizations to ensure diversity and inclusion actions?

I strongly believe in the power of teaching by example, and I think {reprograma} is leading the way thanks to our diverse management and teaching teams as well as our inclusive selection criteria for students. If we can do it and do it successfully, we know it will inspire other organizations to do the same.

However, this is not enough on its own. {reprograma}   has also established a team that is dedicated to placing our graduates in organizations and companies that are recognized for their excellent diversity, equity and inclusive policies. 

{reprograma}works with a number of partner companies. These relationships are mutually beneficial as they advise us on the skills they need from our students, and their new hires while we help them to become truly inclusive employers. 

For us, this means advising companies on the design and implementation of their inclusion and diversity strategies, or on how to communicate about issues like unconscious bias or gender equality to change mindsets and company culture.

We are proud to be creating an ecosystem that is inspiring companies to put diversity, equity and inclusion at the heart of their business.

How has winning the UNESCO Prize for Girls' and Women's Education in 2021 helped advance your work?

{reprograma} has gone from strength to strength since winning the UNESCO Prize for Girls’ and Women’s Education in 2021. 

Thanks to the US$ 50,000 prize money we were able to enhance our curriculum and to start a career mentoring programme for girls aged between 14 and 18. However, the recognition and credibility that the Prize has given us as an organization has been invaluable.

This recognition has amplified our voice both in Brazil and on the world stage and enabled us to establish crucial new partnerships that are helping us to expand our programmes into other countries in Latin America. 

The UNESCO Prize for Girls’ and Women’s Education honours outstanding and innovative contributions made by individuals, institutions, and organizations to advance  girls’ and women’s education . It is the first UNESCO Prize of this nature and is unique in showcasing successful projects that improve and promote the educational prospects of girls and women and in turn, the quality of their lives.

The 2024 call for nominations close on 24 May. Find out more about the selection criteria and how to be nominated on the Prize’s  website . 

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MIAMI — Shiva Rajbhandari doesn’t want you to think there’s anything impressive about the fact that he ran for a school board seat at age 17.

He doesn’t want you to consider it remotely awe-worthy that he campaigned on a platform to turn his Idaho district into a leader on climate change, or that he won, against an incumbent, in the highest-turnout school board election in Boise history.  

What’s impressive, he says, are his Boise public school teachers, who educated him on climate change beginning in seventh grade, not because of any state science guidance but because they recognized its importance. They also “told me every single day that your voice is powerful, that you can make a difference,” he said. 

“This is something that should be accessible to every student,” Rajbhandari, now 19, told an audience at the Aspen Institute’s annual climate event earlier this month. But “not every student has that.”

Rajbhandari, like many of those I spoke with at the Miami event, sees education as fundamental to reducing the harms of a warming planet. By giving young people the skills and resilience to fight climate change, and by harnessing school systems – often among the largest employers and landowners in communities – to reduce their carbon footprint, education can unleash positive changes for a less-apocalyptic future.

“We must recognize that education is the climate solution,” said Rajbhandari, who spoke on a panel organized by This is Planet Ed , an Aspen project that has pushed to get education on the climate agenda and vice versa.

Here are some of my takeaways from the conference, both in terms of how climate change is affecting students and learning, at all education levels, and how education systems can tackle the problem.

Early education:

• Danger lurks for the youngest kids: Kids ages zero to 8 are especially vulnerable to climate change and its harms, such as heat waves; it’s also when kids’ brains are developing most rapidly and laying the foundation for climate resilience is especially critical, said Michelle Kang, chief executive officer for the National Association for the Education of Young Children.
• No need to wait until kindergarten: Kids can be introduced to activities like composting and recycling,and values around a healthy planet, at very early ages, Kang said.
• It’s about access, too: Kang mentioned visiting a child care program in Texas that had lost its shade structure in a storm and no longer had a way to take kids outside safely in the heat of the day.
• Money, money, money: The Bipartisan Infrastructure Deal and the Inflation Reduction Act contain hefty financial incentives and support for schools to reduce their carbon footprint through solar rooftops, electric buses and building efficiencies. Many don’t know of those opportunities, speakers said. 
• Confront the topic differently so it’s not just “the polar bears are dying, the seas are warming and the coral reefs are bleaching, and people in sub-Saharan Africa may not in 50 years have enough to eat,” Rajhbandari said. The issue is urgent, immediate and personal, he noted, but students also need to know they can have a positive impact: “The key there is talking about solutions and talking about agency.” 
• Silence won’t help: Laura Schifter, an Aspen senior fellow who leads This is Planet Ed, recalled hearing from a student who’d become alarmed by a U.N. report about climate change and was shocked that no adults in her school were talking about it. “She started to think, am I the crazy one, that I’m so worried and no one else is worried?” Schifter said. 
• A perfect storm: Climate threats are sharpening the focus on other threats to public schools, like expanded school choice and vouchers. Luisa Santos, a Miami Dade school board member, noted public schools in the city serve as hurricane shelters. School privatization could complicate that role if fewer school buildings are district run and are instead led by many different private operators, she noted.

Higher education:

• New world, new needs : Climate change is starting to reshape the workforce, with new opportunities in renewable energy, sustainability and other sectors, speakers said. Higher ed needs to identify these new needs and help prepare students to fill them, said Madeline Pumariega, president of Miami Dade College.
• For example: She noted that her college started a program for automotive technicians focused on electric vehicles: “We can have the workforce so we don’t find ourselves saying, well sorry, we were trying to do this but we didn’t have the workforce to be able to.”
• Changing existing programs: Colleges are increasingly infusing climate studies into an array of fields – culinary students need to learn about reducing food waste, while future nurses need to know about mitigating the health effects of climate change, speakers said.
• Change begins on campus: There’s also a push to incorporate campus sustainability efforts into coursework. At University of Washington at Bothell, for example, students in several majors worked to restore campus wetlands. At Weber State University, in Ogden, Utah, students in engineering and other fields helped make buildings more efficient. And SUNY Binghamton offers a class called “Planning the Sustainable University” in which students have developed dorm composting, improved furniture reuse rates, and more. 

It’s sobering to contemplate climate change, especially from Miami, where sea level rise threatens to swamp much of the city in the coming decades. But I was reminded of messaging I heard at last year’s Aspen conference, from Yale University senior research scientist Anthony Leiserowitz: “Scientists agree, it’s real, it’s us, it’s bad, but there’s hope.”

Important sources of that hope are students, educators and school systems.      

This story about  climate change solutions was produced by   The Hechinger Report , a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for the   Hechinger newsletters.

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