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Still Separate, Still Unequal: Teaching about School Segregation and Educational Inequality

By Keith Meatto

• May 2, 2019

Racial segregation in public education has been illegal for 65 years in the United States. Yet American public schools remain largely separate and unequal — with profound consequences for students, especially students of color.

Today’s teachers and students should know that the Supreme Court declared racial segregation in schools to be unconstitutional in the landmark 1954 ruling Brown v. Board of Education . Perhaps less well known is the extent to which American schools are still segregated. According to a recent Times article , “More than half of the nation’s schoolchildren are in racially concentrated districts, where over 75 percent of students are either white or nonwhite.” In addition, school districts are often segregated by income. The nexus of racial and economic segregation has intensified educational gaps between rich and poor students, and between white students and students of color.

Although many students learn about the historical struggles to desegregate schools in the civil rights era, segregation as a current reality is largely absent from the curriculum.

“No one is really talking about school segregation anymore,” Elise C. Boddie and Dennis D. Parker wrote in this 2018 Op-Ed essay. “That’s a shame because an abundance of research shows that integration is still one of the most effective tools that we have for achieving racial equity.”

The teaching activities below, written directly to students, use recent Times articles as a way to grapple with segregation and educational inequality in the present. This resource considers three essential questions:

• How and why are schools still segregated in 2019? • What repercussions do segregated schools have for students and society? • What are potential remedies to address school segregation?

School segregation and educational inequity may be a sensitive and uncomfortable topic for students and teachers, regardless of their race, ethnicity or economic status. Nevertheless, the topics below offer entry points to an essential conversation, one that affects every American student and raises questions about core American ideals of equality and fairness.

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Home — Essay Samples — Education — Inequality in Education — Effects of Inequality in Education

Effects of Inequality in Education

• Categories: Inequality in Education

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Published: Jun 13, 2024

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Introduction, body paragraph, perpetuation of poverty, exacerbation of social stratification, undermining democratic ideals.

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Inclusivity in Education: Tackling Inequalities and Promoting Quality Learning

by Omar Mame Diop, Chief of Section and Programme Specialist in Education and Abhinav Kumar.

To read the published version in the SCOONEWS click here

Education offers the simple ability to read, write, count and calculate which plays a vital role in the process of social progress and development. Access to education has the power to improve the quality of life of an individual by providing economic opportunities; changing public perceptions towards human rights; giving a political voice and understanding legal rights- rights, which an individual might already possess but is not able to utilize because of a lack of knowledge and awareness about what it entails. While access to education is essential, the primary aim of schooling is to transfer knowledge and teach skills to students. In other words, it is important to balance an increase in ‘quantity’ of education with a simultaneous increase in the ‘quality’ of education which is accessible and affordable for each and every individual. 

With the vision of “Leaving no one behind”, the Sustainable Development Goals (SDG’s) 2030 Agenda by the United Nations has played a pivotal role in drawing attention to the inequalities which restrict access to quality education across the globe. While SDG 4 and SDG 10 specifically talk about ’Quality Education’ and ‘Reduced Inequalities’ respectively, the remaining 15 SDG’s directly or indirectly highlight the emergent need to build an inclusive environment which provides equitable access to quality education for all. 

Inequalities do not just exist in societies exclusively but in most cases, different forms of inequality intersect with each other and exacerbate the situation for some individuals. For instance, due to prevailing prejudices, a poor woman from an indigenous community living in a rural area is likely to be more disadvantaged than any other individual in the same locality. This highlights social injustice towards individuals within a community based on their gender, caste, location and cultural habitats. It is extremely important to realise that inclusivity is not restricted to providing access to schools by building infrastructure, ensuring school facilities and increasing enrolment. Geographical location; nutrition; mental health; disabilities are some of the many factors which need to be addressed whilst advocating for inclusivity in education.  

While there are policy frameworks laid down by the Government of India to reduce and challenge inequalities, they are either not applied correctly or there are multiple forms of inequalities which make these policies redundant. In the education sector, The Right of Children to Free and Compulsory Education (RTE) Act, 2009 was passed in an attempt to boost primary level education enrolment rates for children aged between 6 and 14 (Government of India 2009). While this has had a positive impact on the enrolment rates in Bihar with student enrolment rates going above 90% for primary level education (Mukul 2015), figure 1 highlights the large number of disparities among different social groups. Nearly 60% of the Schedule Caste (historically termed as ‘socially backward communities’ in India) remain to be illiterate while the ‘general’ category seems to have better access to quality education with a 20% figure. Consequently, these differences tend to restrict access to other social protection systems in the long run. This implies the need to amend policies in a way which creates equal opportunities for every individual in the country, regardless of her/his economic status or social identity.

Making foundational learning part of ‘Inclusive Education’

Inclusivity is also to be met with quality learning outcomes. The World Development Report 2018 entirely focused on the urgent need to promote learning to fully utilize the potential of education (World Bank 2018). The report shares a decline in the learning abilities of students mainly from developing countries and has emphasised on the need to prioritize learning and not just schooling. Amongst the developing countries, with a population of over 1.3 billion people spread across the 28 states and 8 union territories, the challenge of providing equal access to quality education is a tremendous one for India. In fact, as per the latest census data, India has a high child population (0-18 years) percentage (39%) highlighting the increased responsibility on the state for providing equitable access to quality education to all age groups (Government of India 2018).  While this shows that India has a huge challenge to overcome right now, an optimistic way to look at it is that if an ‘efficient’ education system is put in place at the earliest, the country can reap benefits of its high demographic dividend in the long run.

There are multiple pathways to build an ‘efficient’ education system in India. There is substantial evidence at both, international and national level to prove that one of the most effective ways to attain quality education for all is investment in Early Childhood Education (ECE) (OECD 2019). The India Early Childhood Education Impact (IECEI) study, conducted by the ASER Centre and the Centre for Early Childhood Education and Development (CECED), shows that children who have access to high-quality ECE are more ‘school ready’ than those who do not (Kaul et al. 2017). Over and above ECE’s potential to improve linguistic, cognitive and socio-emotional skills of the child, ECE is also extremely beneficial for the mother, the family and the national economy in the long run (OECD 2017).

Despite increasing evidence that ECE contributes towards better education, social, health and economic indicators; universalization of pre-primary education was not given the priority it requires in India until recently. The draft National Education Policy (NEP) 2019 has stated that the learning gaps start even before children attend school. It has identified foundational learning as the root cause of the learning crisis in the country and it is now upon state governments to anticipate and simultaneously react to the challenges ahead in providing foundational literacy and numeracy skills to make all young children ‘school ready’.     In order to make sure a holistic approach towards inclusivity in education, UNESCO defines inclusive education as- “Inclusion is seen as a process of addressing and responding to the diversity of needs of all learners through increasing participation in learning, cultures and communities, and reducing exclusion within and from education. It involves changes and modifications in content, approaches, structures and strategies, with a common vision which covers all children of the appropriate age range and a conviction that it is the responsibility of the regular system to educate all children” (UNESCO 2005).

In its efforts to address inclusivity, the Government of India passed the Rights of Persons with Disabilities (RPWD) Act, 2016 which identified the types of disabilities have been increased from 7 to 21 and that the Central Government will have the power to add more types of disabilities. This was a great step taken in addressing inclusive education as it went beyond the physical aspects of disability and included mental aspects. Inclusive Education had to be rethought and implications of disabilities on learning had to be considered and addressed.

UNESCO New Delhi is committed in promoting and ensuring the need to provide equitable access to quality education for all. Inclusive education comes out of a vision of the world based on equity, justice and fairness. In this regard, UNESCO New Delhi office launched, ‘N FOR NOSE - State of the Education Report for India 2019: Children with Disabilities’, in July 2019. It aims to articulate a vision of education for children with disabilities for 2030 as set out in national and international policy documents and legislative frameworks. Similarly, an annual report on Technical and Vocational Education and Training (TVET) will be released in 2020. As we step up our efforts in the countdown towards achieving the 2030 agenda, we reaffirm the need to form an education system which is inclusive by tackling social, cultural, economic and spatial inequalities within countries. Concerted and multi-sectoral efforts are the need of the hour to ensure the fulfilment of the SDGs’ pledge of ‘leaving no one behind’.

In 2020 and during a period where almost all countries are going through a crisis situation due to Covid19, it is our duty to reflect on the difficulties of those people who cannot switch to e-learning methods due to their inability to access the internet, computers and laptops or even lack of knowledge about online learning courses. As we advocate for education for all in such testing times, we need to ensure that individual from all backgrounds are made part of the education ecosystem which can further empower them to fight situations like these in the future.

To face the COVID-19 crisis, UNESCO has provided immediate support to countries by updating the distance learning guides for more than 1.47 billion children who are out of school because of school closures across the globe (UNESCO 2020).

As a right, learning must continue and the efforts should go more to those who are the most disadvantaged. There is an urgent need to emphasize the role of education in responding to such crises. UNESCO New Delhi Education team will continue to think and reflect on:

• How to ensure the continuity of learning for all even in times of crisis/emergency
• How to train teachers for their preparedness and what to include in the content of their education
• How to organize distance education, home schooling and personalized pathways.

Related items

• UNESCO Office in New Delhi
• SDG: SDG 4 - Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

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• v.117(32); 2020 Aug 11

A century of educational inequality in the United States

Michelle jackson.

a Department of Sociology, Stanford University, Stanford, CA, 94305;

Brian Holzman

b Houston Education Research Consortium, Rice University, Houston, TX, 77005

Author contributions: M.J. and B.H. designed research; M.J. and B.H. analyzed data; and M.J. wrote the paper.

Associated Data

The analysis code and auxiliary data required to produce the figures and tables in this paper can be accessed at https://osf.io/jxne5 . Code to produce estimates for each of the individual datasets (see Table 1 ) is also provided. Details on how to access these datasets are provided in SI Appendix (most datasets are available for download upon registration with the data provider, while others are accessible only with a restricted use license from the National Center for Education Statistics).

Significance

There has been widespread concern that the takeoff in income inequality in recent decades has had harmful social consequences. We provide evidence on this concern by assembling all available nationally representative datasets on college enrollment and completion. This approach, which allows us to examine the relationship between income inequality and collegiate inequalities over the full century, reveals that the long-standing worry about income inequality is warranted. Inequalities in college enrollment and completion were low for cohorts born in the late 1950s and 1960s, when income inequality was low, and high for cohorts born in the late 1980s, when income inequality peaked. This grand U-turn means that contemporary birth cohorts are experiencing levels of collegiate inequality not seen for generations.

The “income inequality hypothesis” holds that rising income inequality affects the distribution of a wide range of social and economic outcomes. Although it is often alleged that rising income inequality will increase the advantages of the well-off in the competition for college, some researchers have provided descriptive evidence at odds with the income inequality hypothesis. In this paper, we track long-term trends in family income inequalities in college enrollment and completion (“collegiate inequalities”) using all available nationally representative datasets for cohorts born between 1908 and 1995. We show that the trends in collegiate inequalities moved in lockstep with the trend in income inequality over the past century. There is one exception to this general finding: For cohorts at risk for serving in the Vietnam War, collegiate inequalities were high, while income inequality was low. During this period, inequality in college enrollment and completion was significantly higher for men than for women, suggesting a bona fide “Vietnam War” effect. Aside from this singular confounding event, a century of evidence establishes a strong association between income and collegiate inequality, providing support for the view that rising income inequality is fundamentally changing the distribution of life chances.

It has long been suspected that the takeoff in income inequality has made the good luck of an advantaged birth ever more consequential for accessing opportunities and getting ahead. The “income inequality” hypothesis proposes that intergenerational inequality—with respect to educational attainment, social mobility, and other socioeconomic outcomes—will increase as income inequality grows. Because this hypothesis shot to public attention with Krueger’s ( 1 ) discussion of the Great Gatsby curve, the proposition that high levels of income inequality have generated correspondingly high levels of intergenerational reproduction is now a staple of public and political discourse. Despite the prominence of this argument, the evidence in its favor is less overwhelming than might be assumed ( 2 ), and is largely limited to the empirical result that intergenerational income inheritance has increased in recent decades, at least in some analyses ( 3 , 4 ). Even this result has been contested and is far from widely accepted ( 5 ).

In this paper, we assess the plausibility of the income inequality hypothesis by examining changes over the past century in the income-based gaps in college enrollment and completion. This is a field in which descriptive evidence is key: Designs that would allow for convincing causal inference are in short supply, and where designs are available, the data are not. And yet most of the descriptive evidence in regard to the college level pertains only to recent decades, when both income inequality and collegiate inequalities have increased (refs. 6 – 8 ).

The trends through earlier decades of the century, within which the great U-turn in income inequality occurred, remain largely undocumented. To overcome this evidence deficit, we might be inclined to draw on evidence on other educational outcomes, such as test scores and years of schooling. Reardon’s analysis of family income test score gaps, for example, shows steadily rising gaps between cohorts born in the 1940s and those born in the present day (ref. 9 ; cf. ref. 10 ). But test scores are quite imperfectly correlated with educational attainment, and evidence from studies of inequalities in years of schooling would support different conclusions on trend. Hilger’s ( 11 ) analysis of long-term trends using Census data shows that there was a decline in the effects of parental income on child’s education between the 1940s and 1970s, while Mare ( 12 ) shows an increasing effect of family income on higher-level educational transitions for midcentury cohorts as compared to early-century cohorts. Taking these studies together, it is difficult to reach any firm conclusion about the income inequality hypothesis, as one might infer an increase, a decrease, or stability in collegiate inequalities during the midcentury, depending on which study is considered.

Extending the time series over the whole of the past century allows for a fuller assessment of the income inequality hypothesis, as the long-run historical series on income inequality exhibits a relatively complicated pattern, as opposed to the simple increase in the recent period. In much the same way as the magnitude of changes in income inequality could only be appreciated when considered in the long run, current levels of educational inequality must be evaluated and understood in full historical context ( 13 ). In a comprehensive extension of previous research on collegiate inequalities, we thus use all nationally representative data sources that we were able to locate and access. This strengthens the descriptive evidence that can be brought to bear upon the income inequality hypothesis.

In the following sections, we discuss the available data and the methods of analysis, and present our results on long-term trends in collegiate inequalities. We will focus on inequalities in completion of 4-year college, enrollment in 4-year college, and enrollment in any college (2- or 4-year). We will demonstrate an essential similarity in inequality trends across the range of collegiate outcomes. Although we will show that income inequality is strongly associated with inequalities at the college level, we will also highlight that it is not the only force at work.

College Enrollment and Completion in the Twentieth Century

The twentieth century was the first century in which education systems were widely diffused and, at least in principle, accessible to all social groups. The century witnessed substantial expansion at the college level: The college enrollment rate for 20- to 21-y-olds increased from around 15 % for the mid-1920s birth cohorts to almost 60 % for cohorts born toward the end of the century. * As Fig. 1 shows, rates of enrollment rose rapidly for cohorts born in the early century to midcentury, and flattened out and even declined for the midcentury birth cohorts, before resuming a steady increase for cohorts born in the later decades of the century.

Proportion of birth cohort enrolled in college ages 20 y to 21 y ( 14 ), and proportions completing 2- and 4-year college degrees, Current Population Survey March, Annual Social and Economic Supplement ( 15 ).

We see in Fig. 1 a stark reversal of the gender gap in college enrollment; for birth cohorts from the mid-1950s to mid-1990s, the proportion of women enrolled in college grew by around 30 percentage points, while the corresponding increase for men was just under 20 percentage points ( 16 , 17 ). The reversal occurred immediately after the rapid increase in enrollment rates observed for male birth cohorts at risk for service in the Vietnam War ( 16 ). A literature in economics has demonstrated that men born in the 1940s and 1950s were unusually likely to attend and graduate from college, although there is disagreement with respect to whether the observed increase in men’s college participation rates should be attributed to draft avoidance or to postservice GI Bill enrollments (ref. 18 ; cf. ref. 19 ).

Alongside trends in college enrollment, Fig. 1 presents rates of college completion by type of degree. While rates of completion of 2-year college are rather flat for cohorts born from the 1950s onward, rates of 4-year college completion have increased considerably. As the figure suggests, rates of 4-year college completion are highly correlated with rates of enrollment, but research shows that, over the past half-century, rates of college completion increased less sharply than rates of enrollment, because the college dropout rate increased ( 6 , 20 ).

Materials and Method

Although it is relatively straightforward to examine changes in rates of college enrollment and completion over time, it is rather less straightforward to examine income inequalities in collegiate outcomes across the span of the twentieth century, because data on parental income, college enrollment, and college completion are not routinely collected in government surveys. We must therefore piece together the trends in collegiate inequalities through the analysis of available sources of nationally representative data. We include results from the analysis of both cross-sectional surveys of adults and longitudinal surveys beginning with school-aged children, and, for a number of recent cohorts, we calculate estimates from tax data results in the public domain. Although this approach presents obvious challenges as regards comparability of data sources and measures, for much of the period that we cover, we have multiple estimates of collegiate inequalities for any given period of time. The datasets and their key characteristics are listed in Table 1 ; detailed descriptions of each dataset are included in SI Appendix .

Characteristics of the datasets included in the analysis

Add Health, National Longitudinal Study of Adolescent to Adult Health; ELS, Education Longitudinal Study; HSLS, High School Longitudinal Study.

The datasets cover cohorts born between 1908 and 1995, and it is only at the beginning and the end of the data series that our birth cohorts are represented by no more than one dataset. Although we aim to define cohorts according to year of birth, for some of the datasets we must construct quasi-cohorts based on age or grade, because year of birth was not recorded.

The biggest constraint that we face in analyzing income inequalities in collegiate attainment relates to gender. Data on the earlier birth cohorts come from the Occupational Changes in a Generation (OCG 1973) survey, which was administered in conjunction with the Current Population Survey ( 21 ). This survey was completed by men only, so we lack information on the educational attainment of women in the earliest birth cohorts. By presenting all results separately for men and women, patterns over time can be compared by gender.

The datasets were prepared to provide consistent measures of family income, college enrollment, and college completion. We produce simple binary variables that capture whether an individual completed a 4-year degree, whether an individual enrolled in (without necessarily completing) a 4-year degree program, and whether an individual enrolled in (without necessarily completing) a college program. Unfortunately, the tax data results pertain only to college enrollment per se, so we have fewer available data points for the analyses of 4-year completion and enrollment than for the analyses of enrollment in any college program. All samples are restricted to individuals who enrolled in high school, in order to maximize consistency across samples. In SI Appendix , we also include results for a smaller sample restricted to high school graduates ( SI Appendix , Fig. S6 ).

A more difficult variable to harmonize over time is family income. Although in some datasets family income is measured directly (e.g., annual net family income in dollars), in many of the available datasets family income is measured only as an ordinal variable. For these datasets, we employ the method used by Reardon ( 9 ) to calculate test score gaps from coarsened family income data; the method uses the proportions in each income category to assign an income rank to all of those in a given category, and income rank is then the explanatory variable in the analysis ( SI Appendix , SI Methods ).

We estimate logits predicting college enrollment and completion as a function of family income or income rank. Following Reardon ( 9 ), we fit squared and cubed terms to capture the nonlinear effects of income rank. Using the model, we estimate the enrollment and completion rates of those at the 90th percentile of family income and those at the 10th percentile. We choose the 90 vs. 10 comparison over other ways of defining inequality because it accords with past assessments and with the main source of trend in income inequality ( 9 ). † From these rates, we calculate log-odds ratios capturing, for example, the log-odds of completing a 4-year college degree for the 90 vs. 10 family income comparison.

We would be remiss if we did not note the difficulty in measuring family income reliably, particularly using one-shot measures, which are all that are available in almost all of the datasets that we analyze. Further worries might arise because some of the income measures are retrospective, or because the questions are asked of children, not parents. Although we would not minimize the danger of retrospection or of using children’s reports of family income, evidence suggests that child reports of parental socioeconomic characteristics are not substantially worse than parental reports of those characteristics ( 9 , 22 ). Furthermore, the types of errors that individuals make when reporting income appear to have changed very little over time ( 23 ), which is the key issue when mapping trend. To address concerns about the varying quality of the family income data, we multiply all log-odds ratios by 1 / r , where r is the estimated reliability of the family income measure (see SI Appendix , Table S5 for reliability estimates) ( 9 ).

We recognize that “researcher degrees of freedom” are of particular concern when presenting results from a large number of datasets ( 24 ). We provide additional results based on alternative specifications, in SI Appendix , and make our analysis code publicly available on Open Science Framework, https://osf.io/jxne5 .

The Great U-turn in Collegiate Inequality

We now examine collegiate inequalities for cohorts born between 1908 and 1995. Given data constraints, we are limited to examining inequalities over the whole period for men only, but we present results for women for a more limited range of birth cohorts.

In Fig. 2 we present, for the full male series, the estimated probabilities of completing 4-year college at the 90th and 10th percentiles of family income. ‡ We see in Fig. 2 that the increase in 4-year college degree attainment over the twentieth century was far from equally distributed across income groups. Men from the 90th percentile of family income were at the leading edge of the expansion; the figure shows a rapid increase in college completion rates through the 1940s birth cohorts, then a tailing off through the 1950s cohorts, followed by a further rapid increase for those cohorts born in the 1960s onward. In contrast, expansion at the bottom of the income distribution was more sluggish; 4-year college completion rates at the 10th percentile were less than 10 percentage points higher for cohorts born at the end of the century than for cohorts born at the beginning.

Probabilities of 4-year college completion at the 90th and 10th percentiles of family income, male birth cohorts, 1908–1986.

Fig. 2 shows that absolute differences in completion rates between income groups increased from the beginning to the end of the century. But this important result must be considered alongside changes over the century in the overall completion rate ( 12 ). Although the probability gap was small at the beginning of the century, the odds of college completion were around 7 times higher for the rich than for the poor, because the rich were able to secure a large proportion of the limited number of college slots. In relative terms, the poor born in the early century were more disadvantaged than their counterparts born in the 1960s, when 90 vs. 10 gaps in the probability of college completion were substantially larger. Although both probability gap and odds-ratio measures are informative, we focus from this point forward on odds-ratio measures of educational inequality, which are margin insensitive and thus feature relative—rather than absolute—advantage. But, in SI Appendix , we present probability plots for the three collegiate outcomes ( SI Appendix , Fig. S1 ), and include analyses based on probability gaps in SI Appendix , Table S3 . The key results hold for both types of analysis.

We plot, in Fig. 3 , the 90 vs. 10 log-odds ratios describing inequalities in collegiate outcomes for each of the datasets in our analyses, with trends estimated from generalized additive models (GAM). The GAMs are fitted to the plotted data points, with each point weighted by the inverse of the SE for the estimate. § In the earlier period covered by OCG, we fit the model to the estimates derived from analyses of single birth cohorts, but present point estimates representing groups of birth cohorts to show the consistency across these specifications. Confidence intervals are presented in SI Appendix , Fig. S2 ; figures showing 90 vs. 50 and 50 vs. 10 inequalities are included as SI Appendix , Figs. S3 and S4 .

The 90 vs. 10 log-odds ratios expressing inequality in 4-year completion, 4-year enrollment, and any college enrollment. ( Left ) Male birth cohorts, 1908–1995; ( Right ) female birth cohorts, 1951–1995.

We focus first on describing the trends for men, for whom we have results spanning the whole century. It is clear from Fig. 3 that the over-time trends are similar across the various collegiate outcomes and, further, that there is no simple secular trend for any of the outcomes under consideration. There are three key attributes of the trends that should be emphasized.

First, Fig. 3 shows that, toward the middle of the century, there was a great U-turn in collegiate inequality. Inequalities fell rapidly for cohorts born in the early to mid-1950s, then bottomed out until the mid-1960s, before ultimately rising steeply for cohorts born from the mid-1960s onward. The U-turn appears to be more pronounced for 4-year and “any college” enrollment than for completion of a 4-year degree, but it is present for all of the collegiate outcomes under consideration.

Had we measured collegiate inequalities in but a single dataset, we might be skeptical that our observed trend was on the mark and, in particular, that there was a rapid fall in inequality for the midcentury birth cohorts. But this trend is supported across all of the datasets from the period: OCG and National Longitudinal Study (NLS) Young Men show high inequality in the early 1950s; Panel Study of Income Dynamics (PSID), NLS72, and High School and Beyond (HS&B) pick up the lower inequality of the mid-1950s to the mid-1960s; and the subsequent uptick in inequality is captured in PSID, the school cohort surveys, and the National Longitudinal Studies of Youth (NLSY79&97). Indeed, Fig. 3 demonstrates that there is great consistency across a large number of different data sources. ¶ At the trough, inequality in 4-year college completion was reduced to a log-odds ratio of around 1.5, indicating that, even in this low-inequality period, the odds of those at the 90th income percentile completing a 4-year college degree were almost 4.5 times greater than the equivalent odds for those at the 10th percentile. Inspection of SI Appendix , Fig. S3 suggests that the U-turn observed in Fig. 3 is largely driven by changes in the top half of the income distribution: the U-turn is rather more pronounced for the 90 vs. 50 comparison than for the 50 vs. 10 comparison.

Second, if skepticism about a midcentury fall in collegiate inequality were to be sustained, suspicion would also have to fall upon all currently accepted results on over-time trends, which demonstrate a substantial increase in inequalities in college enrollment and completion between cohorts born in the midcentury and late century. If we were to impose a simple linear smooth on the century-long data series, this would indicate relatively modest increases in collegiate inequalities over the period taken as a whole (see dashed lines, Fig. 3 ). # Again, because the trends are mapped using multiple datasets, we are confident that the pattern of a U-turn in collegiate inequality is supported.

Third, any evidence of a U-turn must bring to mind the pattern of income inequality over the past century. As Piketty and Saez ( 27 ) described, toward the middle of the twentieth century, the share of income going to the top 10% rapidly declined, before rising again over the later decades of the century. The U-turn in collegiate inequality mimics this trend, although it is notable that, insofar as we see similarity in patterns of income inequality and collegiate inequalities, it is income inequality around year of birth that appears to matter most. But, despite the obvious similarities, there is at least one clear divergence in the pattern of collegiate inequality and income inequality: The U-turn in collegiate inequality comes very late. Income inequality begins to fall in the early 1940s, but inequalities in enrollment and completion begin to decline only for cohorts born in the mid-1950s. Men born in the mid-1940s onward were not just born into a period of low inequality, but they spent most of their formative years in a low-inequality society. Despite this, the evidence shows that collegiate inequality increased substantially for the cohorts born in the 1940s and early 1950s; the log-odds ratios describing inequality are increased by around a third over this short period.

Some of the same key features are visible in the results for women, shown in Fig. 3 , Right , although we only have access to data for women born after 1950. We see a basic similarity with the men’s analyses from the mid-1950s birth cohorts onward: Collegiate inequalities are relatively flat for the 1950s to 1960s birth cohorts, and increase for women born in the 1970s and onward. Just as with men, toward the end of the period we see flat and even declining inequalities in enrollment and completion. There are perhaps some subtle differences in the pattern by gender—the upturn in collegiate inequality begins, for example, several years later for women than for men—but we have little evidence here to support a conclusion of substantial difference in inequality for men and women over this period.

There is one notable difference between the men’s and women’s results, relating to the period when trends in male collegiate inequality substantially diverged from trends in income inequality. This exceptional period appears to be exceptional for men, but not for women. Although we cannot track collegiate inequalities for women across the whole midcentury period, the first data points in the female data series (NLS Young Women: 1951–1953 birth cohorts) are lower than the nearby estimates for men (NLS Young Men: 1949–1951 birth cohorts). ** This period of divergence between collegiate inequality and income inequality coincides with the period that we identified above as holding special consequences for men’s educational attainment: Men born in the 1940s and early 1950s were subject to the threat of military service in the Vietnam War.

There are no cohort studies of women that would allow us to compare male and female inequalities in college enrollment and completion throughout this period. We do, however, have access to data on men who fathered children who were at risk for service during the Vietnam War: The NLS Older Men survey can be used to track collegiate inequalities for the children of men who were aged 45 y to 59 y in 1966. The structure of this dataset is somewhat different from the datasets underlying our time series, but we nevertheless find confirmation, in Fig. 4 , that male and female inequalities diverged in the Vietnam years.

The 90 vs.10 log-odds ratios expressing inequality in 4-year college completion, 4-year enrollment, and any college enrollment, men and women born 1935–1943 and 1944–1952, NLS-Older Men data.

In the pre-Vietnam period, male and female collegiate inequalities were of similar magnitude. The log-odds ratio for 4-year enrollment, for example, was 2.3 for men (95% CI: 1.5, 3.1), as compared to 2.4 for women (1.7, 3.2). But, for the birth cohorts at risk for serving in Vietnam, the male log-odds ratio increased slightly, to 2.5 (1.8, 3.2), while inequality fell substantially for women, to 1.4 (0.8, 2.0) (see SI Appendix , Fig. S8 for a figure with CIs). These results provide support for the claim that men’s collegiate inequality was substantially and artificially raised relative to expected levels during this period because of the Vietnam War. Unfortunately, our data are not well-suited to evaluating why male and female collegiate inequality differed in the Vietnam period. But some evidence can be brought to bear on this question by comparing preservice and postservice inequalities in college participation for the men in OCG ( SI Appendix , Fig. S9 ). These data are more consistent with a draft-induced increase in male collegiate inequality than with a GI Bill-induced increase. ††

Bringing the results in Fig. 4 together with what is known about college enrollment and completion patterns during the Vietnam War period, it seems likely that the disproportionate increase in men’s college participation rates observed in Fig. 1 was achieved, at least in part, through a gender-specific change in the effect of family income on college enrollment and completion.

The Association between Income Inequality and Collegiate Inequality.

We now present a formal statistical test of the strength of the association between income inequality and collegiate inequality. We regress the log-odds for collegiate inequalities on income inequality, as measured through the share of wages going to the top 10% ( 27 ). ‡‡ In addition to the income inequality variable, for the full male series (1908–1995), we fit a “Vietnam effect,” with a dummy variable that isolates the cohorts at risk from the draft lotteries (i.e., 1944–1952 birth cohorts). We fit models to the full male series (1908–1995 birth cohorts), a compressed male series (1952–1995 birth cohorts), and the female series (1951–1995 birth cohorts). A full regression table with coefficients and standard errors is included as SI Appendix , Table S4 . §§ In Fig. 5 , we present estimates of the predicted increase in the log-odds ratios for an eight percentage point increase in the share of wages going to the top 10%; this increase is equivalent to the “takeoff” in income inequality that occurred between the midcentury and the 1990s. ¶¶

Predicted increase in collegiate inequality log-odds ratios associated with the top 10%’s share of wages increasing by 0.08 (equivalent to the takeoff in income inequality); 90 vs. 50 (dark gray), 50 vs. 10 (light gray), and 90 vs. 10 (total) comparisons.

The regression coefficients describing the associations between income inequality and 90 vs. 10 collegiate inequalities can be straightforwardly decomposed into two parts: an association between income inequality and the 90 vs. 50 log-odds ratio, and an association between income inequality and the 50 vs. 10 log-odds ratio. In Fig. 5 , the total height of each bar represents the predicted increase in the 90 vs. 10 log-odds ratio for an eight percentage point increase in income inequality, while the dark and light gray bars show the predicted increases in the 90 vs. 50 and 50 vs. 10 log-odds ratios, respectively.

Examining first the results for the 90 vs. 10 comparison, we see confirmation of a relatively strong association between income inequality and collegiate inequality over the full sweep of the twentieth century. For women, for example, the model predicts that an increase in income inequality equivalent to that observed in the takeoff period would increase the 90 vs. 10 log-odds ratio by around 1 for 4-year enrollment and completion, and by around 1.3 for enrollment in any college. Although there is variation in the strength of the association for the different outcome measures, the income inequality effects are large and positive in all of the analyses, indicating substantial support for the income inequality hypothesis.

Given that the takeoff in income inequality was largely characterized by the top of the income distribution moving away from the middle and bottom of the distribution, the income inequality hypothesis would predict larger effect sizes for the 90 vs. 50 comparison than for the 50 vs. 10 comparison. When we decompose the 90 vs. 10 results into 90 vs. 50 and 50 vs. 10 components, we see precisely this result. The income inequality effects for the 90 vs. 50 comparisons in all cases outweigh those for the 50 vs. 10 comparisons, particularly in the analyses of 4-year college enrollment and completion.

But the results also provide grounds for exercising caution when interpreting differences in effect sizes across the models, as the effect sizes in the full and compressed male series are more similar for the “any college” analyses than for the 4-year analyses, where the sample sizes are smaller. Even when analyzing all available datasets and exploiting the full range of variation in income inequality over the century, our statistical power is limited. This is even more clear when we extend the models summarized in Fig. 5 to include additional macro-level regressors that social scientists have previously used to predict inequalities at the college level. These additional variables include the economic returns to schooling, which are assumed to influence individual decisions about whether or not to invest in college education ( 33 ), and the high school graduation rate, which has been shown to influence educational expansion at the college level ( 34 ). As shown in SI Appendix , Table S1 , estimates from these models are more volatile, particularly for women.

The volatility arises because some of our analyses are, like past analyses, limited to more recent cohorts in which the takeoff assumes a monotonically increasing form. This makes it difficult to adjudicate between the large number of monotonically increasing potential causes. An important advantage of our full-century approach is that it reaches back to a time in which these competing causes did not always move together. In Fig. 6 , we present the results of a simulation exercise, in which we run 1,000 regressions for a range of different model specifications on the full and compressed male series, with each regression including a new variable containing random numbers drawn from a normal distribution ( μ = 0; σ = 1). We examine the stability of the income inequality effects with respect to inequality in college enrollment, for which we have the largest number of data points. We add to the basic model in Fig. 5 controls for time, either in the form of 1) a linear effect of year or 2) dummies for decades, and measures of the returns to schooling ( 33 , 35 , 36 ) and the high-school graduation rate ( 34 , 37 ).

Predicted income inequality effects (coefficients × 0.08) from 1,000 regressions of 90 vs. 10 inequality in “any college” enrollment on income inequality and random number variables, for various model specifications, for full and compressed series, men only. Models: 1, Inequality; 2, Inequality+year; 3, Inequality+controls; 4, Inequality+controls+year; and 5, Inequality+controls+decade.

As Fig. 6 shows, the income inequality effects estimated for the full male series are robust to the inclusion of other potential confounding variables. But Fig. 6 also highlights the extent to which a proper evaluation of the income inequality hypothesis requires researchers to exploit all of the available data. Although the bivariate analysis shows a similar effect of the income inequality variable in both the full and compressed series, the effects are a good deal more volatile in the more highly parameterized models in the compressed relative to the full series. *** The substantive implication of this analysis is clear: It is only with the full data series that we obtain relatively precise and reliable estimates of the association between inequality in collegiate outcomes and income inequality.

We have examined descriptive evidence on the association between inequality in collegiate attainment and income inequality over the past century. Although there has been much recent interest in the income inequality hypothesis, it has been difficult to make headway because commonly used datasets pertain only to recent decades, when income inequality was increasing. We have thus proceeded by reaching back to the very beginning of the twentieth century, assembling all of the available datasets, and harmonizing the variables in these datasets.

The results show that collegiate inequalities and income inequality are, in fact, rather strongly associated over the twentieth century. Just as with income inequality, we see evidence of a U-turn in 90 vs. 10 collegiate inequality, and evidence of a substantial takeoff in collegiate inequalities in recent decades. When we examine trends in 90 vs. 50 and 50 vs.10 inequalities, we find that the 90 vs. 50 trends mirror the 90 vs. 10 results. Taken together, our results offer solid descriptive support for the income inequality hypothesis.

Inequalities in collegiate attainment increased hand in hand with the expansion of college education in the United States. Rates of college enrollment and completion were higher at the end of the century than they had been at any time in the preceding hundred years, and yet, for these birth cohorts, we see substantial inequalities, as captured in both percentage point gap and odds ratio measures. In point of fact, the only time during the twentieth century for which we observe a reduction in educational inequality is during the period when expansion at the college level had paused. Although the counterfactual is obviously not observable, these results emphasize the importance of attending to the distribution of college opportunities in addition to overall levels of attainment. These distributional questions will take on even greater significance in the context of the economic and social crisis engendered by coronavirus disease 2019, a crisis that is likely to have enduring effects on both the distribution of income and access to the higher education sector.

Our analyses are not well suited to evaluating the mechanisms generating the association between income inequality and collegiate inequalities. However, given the pattern of collegiate inequality across the century, we suspect that a mechanical effect is likely to be responsible. If money matters, as we know it does, and growing income inequality delivers more money to the top, then, all else being equal, these additional dollars would in themselves produce growing inequality in college enrollment and completion. The mechanical effect is therefore a parsimonious account of the trend that we see here ( 8 ). That the over-time associations are substantially stronger for the 90 vs. 50 comparison as compared to the 50 vs. 10 comparison provides further suggestive evidence in this regard. Nevertheless, there is a period for which we undoubtedly hypothesize an increase in the relational effect of income: the Vietnam War. For the war to lead to increased collegiate inequality, the effect of income on educational attainment would have to increase, particularly given that income inequality was low and stable for these birth cohorts.

Whatever the mechanisms may be, the key descriptive result is that, over the course of the twentieth century, a grand U-turn in collegiate inequality occurred. Cohorts born in the middle of the century witnessed the lowest levels of inequality in college enrollment and completion seen over the past hundred years. Contemporary birth cohorts, in contrast, are experiencing levels of collegiate inequality not seen for generations.

Supplementary Material

Supplementary file, acknowledgments.

We thank David Cox, David Grusky, and Florencia Torche for their detailed comments on earlier versions of this paper, and also Raj Chetty, Maximilian Hell, Robb Willer, the Cornell Mobility Conference, the Stanford Inequality Workshop, the Stanford Sociology Colloquium Series, and University of California, Los Angeles’s California Center for Population Research seminar for useful suggestions. Additionally, we thank Stanford’s Center for Poverty and Inequality, Russell Sage Foundation and Stanford’s United Parcel Service (UPS) Fund for research funding, Stanford’s Institute for Research in the Social Sciences for secure data room access, and the American Institutes for Research for data access. We are grateful to the editor and reviewers for their helpful and productive suggestions.

The authors declare no competing interest.

This article is a PNAS Direct Submission. E.G. is a guest editor invited by the Editorial Board.

Data deposition: Code for data analysis is archived on Open Science Framework ( https://osf.io/jxne5 ).

*Throughout this paper, we use the term “college” as a shorthand for “2- or 4-year college.”

† We also include results based on comparing income quartiles in SI Appendix , Fig. S5 .

‡ The probabilities are estimated from the logit model, and we fit a GAM to establish trend. See SI Appendix , SI Methods for more details.

§ We determine the appropriate number of degrees of freedom for the trend lines by fitting a series of GAMs and comparing model fit (using the Akaike Information Criterion). For the analysis of college enrollment for male birth cohorts, we use the stepwise model builder in R’s gam package to find the best-fitting model ( 25 , 26 ). As we have fewer point estimates in the other analyses, the stepwise approach is less reliable, and we therefore choose smoothing parameters that provide a reasonable (and conservative) summary of the trend.

¶ It is also clear that some datasets are outliers from the trend. It is not surprising to see variation across samples, and we highlight this variation only because it illustrates a potential danger of using but one or two datasets to establish a trend. The estimates for National Education Longitudinal Study (NELS) (1974), for example, are substantially higher than the surrounding estimates based on one-shot income measures, and there is a surprising degree of cross-cohort volatility in the PSID estimates.

# The linear trend is strongest for 4-year completion, and weakest for enrollment in 4-year college. For all collegiate outcomes, the GAM offers a significant improvement in fit over the simple linear model.

**It would be possible to track male and female educational inequality with respect to parental education or socioeconomic index scores (SEI) ( 28 ), but the sample sizes are, unfortunately, too small for a detailed analysis of gender differences in educational attainment by birth cohort. This approach is also unattractive given that parental education, parental income, and SEI were only weakly correlated in this period ( 29 ).

†† Note that, while previous research has suggested that high-socioeconomic status (SES) individuals might have taken advantage of the GI Bill to a greater extent than low-SES individuals ( 30 ), SI Appendix , Fig. S9 provides little evidence that collegiate inequality was substantially affected. See SI Appendix for further discussion of this point.

‡‡ We choose the wages measure because, for the bottom of the income distribution, wages are a more important component of income than the types of income included in the alternative measures (e.g., capital gains). We measure wage inequality in year of birth. Surprisingly, given the prominence of the income inequality hypothesis, there is not yet adequate guidance in the literature as to the age at which income inequality most influences outcomes, although in the “money matters” literature there has been particular emphasis on the prenatal period, the postnatal period, and early childhood as the lifecourse moments when money matters most ( 31 , 32 ).

§§ In the 4-year analyses, we weight the data by the inverse of the standard errors underlying the estimates. In the analysis of any college enrollment, we do not weight the data, as this data series includes the tax data estimates. Given the size of the samples underlying these estimates, weighting would allow the relationship that pertains in the tax data for cohorts born in the 1980s and 1990s to have a disproportionate influence on the estimated century-long relationship between income inequality and inequality in college enrollment.

¶¶ The estimates in Fig. 5 are obtained by multiplying the income inequality coefficients in SI Appendix , Table S4 by 0.08.

***See SI Appendix , Fig. S10 for similar figures for 4-year enrollment and completion.

This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1907258117/-/DCSupplemental .

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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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Educational Inequality in America: Race and Gender

• Robin N Hamilton
• | December 15, 2020

Education Inequality: Definition and Background       

Educational Inequality is about the disparity of access to educational resources between different social groups. Some examples of these resources include school funding, experienced and qualified educators, books, technologies and school facilities such as sports and recreation. Educational inequality in America are often the result of some of the following factors:

• Government policies
• Choice of school
• Family wealth
• Residential location
• Parenting style and choices
• Implicit bias towards a student’s race, ethnicity and gender

While there are many more factors that contribute to the existence of inequality in the American education system, the broader problems created as a consequence are among the biggest problems faced by America and Americans today. For example, research indicates a direct correlation between education inequality, income inequality, crime rates and prison populations, homelessness, and unemployment. And that’s just the tip of the iceberg.

Educational inequality is hard to quantify. Measuring educational efficacy and the success and fairness of the education policy may vary between countries and states. In the United States, grades, GPAs, various test scores, college entrance and completion statistics, and dropout rates are some of the common statistics that form the basis of educational research.

It is important to note that these statistics are often measures of individual performance and abilities and may not always be indicators of the fairness of the education system. To see the bigger picture in terms of efficacy, most scholars agree that factors beyond academic performance – achievement of learning objectives, learning of skills, fairness of opportunities, and professional life readiness and capabilities – should also be considered.

It can be argued that these factors are more important in terms of measuring the disparity in the quality of education, as academic performance tends to be a reflection of a student’s capabilities instead of the result of policies and social problems. There are visible differences in the quality of education available across economic, racial, and gender lines.

There are calls to reform education systems all across the world. However, this has been a slow and difficult process due to social, cultural and economic practices that are deeply rooted in history. This has made the inequality considerably difficult to eradicate.

A good education is about more than just academic and professional performance and opportunities. It is an important part of moving forward towards becoming a more civilized and humane society. It is also ethically important.

Education should also be about creating awareness of the plight of others and about creating an inclusive and peaceful world that is devoid of injustice and discrimination.

Education Inequality: Facts and Statistics

• The US spends more on education than other Organization for Economic Cooperation and Development (OECD) countries. The US, on average, spent 39% more on elementary and secondary education than other OECD countries. However, this extra spending doesn’t always translate to better educational outcomes.
• By 2022, a 33% growth in the number of Hispanic students enrolled in public schools is expected comparison to  2011, whereas the overall number of non-white students is expected to grow by 44%. For an education system designed to prioritize white students, this exponential increase could prove to be disastrous.
• 69% of Black students
• 73 % of Hispanic students
• 86% of Caucasian students
• 88% of Asian students
• More than 90% of students from low income backgrounds rely on their school as the primary source of high speed internet access. Because of unequal funding, nearly 40 million students do not have access to high speed internet in school. With internet becoming an essential part of modern learning, this disparity has far reaching consequences.
• What is the importance of education as a prerequisite for good career opportunities? Nearly 85% of current and 90% of new jobs require some degree of education.
• Only about half of the students that enroll in a 4 year college degree are expected to graduate within 6 years of enrollment.
• Black students are more likely to be held back in formal schooling. Figures from 2011-12 indicate that 34% of all students held back in grade 9 were Black. In general, Black students are nearly 3 times more likely to be held back than their white peers. They’re also more likely to drop out of high school.
• Black American students are three times more likely to be suspended and expelled. While they only make up 16% of school enrollment, they account for nearly 32% of in-school suspensions, 42% of out-of-school suspensions and 34% of expulsions.
• Schools serving a higher number of minority students tend to have less-experienced, worse paid and less-qualified teachers.
• In many cases there is direct correlation between race, residential location and quality of education. Black children are much more likely to live in low-income households and neighborhoods than their white and Hispanic peers. Almost 25% of Black parents report living in unsafe neighborhoods in comparison to 7% of white parents.
• 16% of Black students drop out from high school in comparison to 8% of white students.
• In the age group of 16 to 24, only 56% of Black students are likely to enroll in a 2 or 4 year college course in comparison to 66% of white students. More Black students tend to drop out of college or take longer to complete graduation.
• Male students largely outnumber female students in Science, Technology, Engineering and Mathematics (STEM) subjects. Gender based stereotypes, dissatisfaction with teaching approaches and lack of encouragement and self-confidence are some of the main contributors.
• Gender based discrimination is often systematic and goes beyond graduation. Gender norms, networking trends, parenthood and other social factors are significant contributors. For example, only 9% of nurses are male while only 4% of women work in sheriffs’ departments.
• College degree completion rates are lower for non-white American women than their white peers, and are higher than their male counterparts. However, the quality of education, attitudes towards education and the opportunities available to women are not on the same level as men.

Education Inequality: Gender

Many, if not most, women face inequality and gender-based discrimination in some form at some point in their lives. As a result, a lot of women are unable to lift themselves out of poverty and improve or change their living conditions. Most women in many parts of the world are still dependent on male family members (fathers, brothers, husbands and sons) for their economic well-being.

Numerically, in North America, Latin America, Caribbean and other western nations, girls are as likely as boys to enroll in and complete schooling. In fact, figures from the United States show that female students are nearly 8% more likely to complete a bachelor’s degree than their male peers. This, then begs the question – Is gender based inequality in the United States a thing of the past? No. Inequality exists, but it is more complicated than in other parts of the world where women are still fighting for the right to education.

As stated previously, boys/men are more likely to be interested in and to excel at STEM subjects. However, studies between 1998 and 2011 indicated that there were no differences in the average math tests scores based on gender for kindergarten students. But a significant gap starts to emerge in favor of male students by the 2 nd or 3 rd grade. This makes it quite obvious that there is something going on in schools that contributes to this.

Looking deeper, it becomes clear that one of the contributing factors to this is the beliefs and attitudes towards the genders of their students. Studies found that when boys and girls belonging to the same race and achieving equal scores were compared, the teachers rated the boys as more mathematically skilled. This can also be interpreted as that for a girl willing to be seen as equally capable, she had to perform as well as a boy and has to be seen working harder than him. Research shows that the aforementioned increase in the gender gap in STEM subjects between kindergarten and 3 rd grade accounts for nearly half of the perceived gap growth.

While various reputable studies indicate differences in the educational strengths and weaknesses of boys and girls – for example, girls are stronger at writing while boys seem to be better at mathematics – these negative attitudes tend to harden the gaps. As a result, there is a lot of stereotyping which may hinder educational progress.

Another, often disregarded, factor is the discriminatory attitudes of parents. America is a multi-racial and multi-ethnic society with a rich diversity in the cultural backgrounds of the populace. Some American households, where budget is a factor, may give more preference to the education of one gender over the other: typically male over female. There are also, often, restrictions over what academic subjects, courses of degrees a male or female student should follow.

Some argue that these cultural differences may contribute to inequality in the American education system, but should not be treated as the consequences of a failing system. However, this is where I’d like to add a brief definition of the phrase ‘education system.’

The education system is a term that encompasses all formal and informal sources of learning, grooming and growth… and it starts at home. Government policies fail to recognize these informal institutions as a part of the system and, therefore, miss out on the bigger picture. No policy is complete or will offer a complete solution without taking parents and the family unit on board.

That’s still not the end of the story. Girls and women in schools and colleges are constant victims of harassment and discrimination. There are frequent controversies in regards to what constitutes appropriate school or college attire – usually directed towards women.  Social practices, such as the stigma around a girl or woman having multiple sexual partners, while a male student is often lauded for the same, also contribute to the inequality that is rife in educational institutions.

History of Racial Inequality in American Education

President John F. Kennedy (1962) described education in the United States as an integral and unifying force behind the American way of life. He said that education is the greatest investment a society can make in itself, and is its own reward. However, education, like many other aspects of American society has a turbulent history.

1. Colonial Era and Slavery

The history of education in America is closely linked with religion. Teaching children to read the bible and learn about Puritan Christianity was the earliest form of education in the United States. The primary purpose of the earliest formal education was to try to assimilate, conform and convert indigenous people into European values, religions and standards.

This process included teaching the natives new languages in an effort to force them to give up their own traditions, cultural practices and even their language This continued well into the 20 th century when indigenous children were forced to attend boarding schools in an effort to assimilate entire communities into mainstream American society.

However, education was also used as a weapon to suppress some segments of society. For instance, African-Americans were prevented from learning to read and write so that they did not learn to question and challenge the status-quo. This fear was especially prevalent in the American South, leading to strict laws prohibiting even the most basic education for slaves.

Some religious groups often attempted to form schools for African Americans. However, this was met with opposition.

2. Civil War and Reconstruction

In comparison to many of the Southern states, attitudes towards the education of minorities and, in particular, African Americans were much more progressive. However, it wasn’t until the Reconstruction Era (after the end of the Civil War) that Black Americans started to gain access to widespread formal education.

Despite the many challenges, the newly freed African Americans sought education as a priority and as means of socio-economic progress and empowerment. However, even then the educational system wasn’t devoid of inequality. While the number of Black students enrolling into educational institutions increased manifold, there was immense inequality in terms of academic and employment opportunities between the different races.

3. Jim Crow Laws

The formal abolition of slavery did not result in an abolition of discrimination targeted towards Black Americans . While slavery was deemed unconstitutional, states were free to enact laws that allowed them to prevent equal opportunities for African Americans.

In this era of segregation, which lasted until the Civil Rights Movement of the 20 th century, Black schools often received less funding than schools meant for white children. This meant that Black students had access to worse facilities, lesser resources and, often, inexperienced and unqualified teaching staff.

This had far reaching consequences for American society and, especially, for African Americans. This meant higher dropout rates, disparity in employment opportunities and higher crime rates especially in predominantly Black neighborhoods.

4. Civil Rights Movement and Integration

The desegregation of the American education system was one of the driving forces behind the Civil Rights Movement. The disparity in the allocation of academic resources, and the discrimination faced by Black students resulted in the creation of various student organizations that played an integral role in the passing of anti-discriminatory laws.

5. The Problems of Today

While there is no doubt that the education system, and American society as a whole, has come a long way towards ending discrimination and injustice, the problems are far from over. Racism , sexism and other forms of discrimination are still prevalent in the American education system. The following sections of this article will highlight the facts, statistics, and the numerous problems with the current education system and propose some well researched solutions.

Intersectionality and Complex Identities

The intersection of race and gender creates a complex web of challenges that compound educational inequalities. Students who are members of historically marginalized ethnic and gender groups frequently experience particular hurdles that overlap and amplify the difficulties they confront. For instance, African American girls may experience preconceptions that limit their academic prowess and leadership ability. Latina students may encounter language barriers that hinder their access to advanced courses. Inequalities in educational results can be sustained by the junction of race and gender, resulting in underappreciated experiences and needs.

LGBTQ+ students and students with disabilities face distinct challenges within the educational system, further exacerbated by their racial and gender identities. Bullying, harassment, and mental health issues are frequent struggles for LGBTQ+ kids, particularly those of color, and can interfere with their academic progress. Discrimination and a lack of inclusive policies may hinder their access to secure and encouraging learning environments.

Similar issues with limited resources and accommodations might arise for students with impairments, particularly those from marginalized racial or gender backgrounds. Stereotypes may limit academic and extracurricular involvement chances by influencing how capable people are seen. Insufficient representation in the curriculum and under-identification of kids needing support can result from the intersection of race, gender, and disability.

Addressing intersectionality requires an inclusive and comprehensive approach to education. Schools must be aware of the variety of identities that students bring and how their experiences are shaped by their ethnicity, gender, sexual orientation, and ability. A more inclusive and equitable learning environment can be achieved by implementing policies that address different dimensions of identity. Additionally, removing the structural obstacles that hinder kids’ academic success can be accomplished by offering them specialized support, mentorship, and resources.

By understanding and actively addressing the intersectional nature of educational inequalities, educators, policymakers, and advocates can work together to create a more just and equitable educational system that uplifts and empowers all students, regardless of their complex identities.

Efforts and Initiatives for Equity

Policy interventions are crucial in addressing educational disparities based on race and gender. Equitable school finance mechanisms, which distribute resources based on the needs of children, especially those in underprivileged communities, are one way to ensure equity. A more inclusive learning environment that reflects the experiences and contributions of all students can also be produced by policies that support diverse and culturally sensitive curricula. Affirmative action regulations can equalize the playing field and help historically underrepresented racial and gender groups gain access to higher education.

Community-based initiatives are powerful tools in the fight for educational equity. Schools frequently work with regional nonprofits and grassroots groups to offer after-school activities, tutoring, and mentoring options for students from underrepresented backgrounds. These programs may provide essential academic assistance and give students the tools to overcome obstacles.

Mentorship programs pair young people with role models of a similar race or gender. Mentors help students overcome obstacles and establish educational objectives by providing advice, encouragement, and a sense of community.

Community-led and activist-led advocacy initiatives aid in bringing attention to educational inequities and advancing structural change. These activists aim to hold educational institutions and legislators responsible for correcting disparities by participating in grassroots organizing, protests, and awareness campaigns.

Collaborative partnerships between schools, communities, and advocacy organizations are essential for creating comprehensive solutions to educational disparities. These initiatives can successfully remove systemic impediments and offer targeted help to students who need it the most by pooling the resources and knowledge of many stakeholders.

To eliminate systemic educational inequalities, efforts and initiatives for equity recognize the significance of a multifaceted strategy. By combining policy changes, community involvement, mentorship, and advocacy, we can work toward a future where every student, regardless of race or gender, has an equal opportunity to thrive.

How to Eradicate Education Inequality

It is a cliché to say that young people are the future. However, a lot needs to be done to help secure a good future for them. Therefore, if inequality persists and reforms are slow to materialize, this future may be in jeopardy. Disadvantaged students, or those that are discriminated against, are two and half times more likely to be low performers on exams. With the numbers female students and students from minority groups on the rise, this lack of performance is likely to grow in a system designed to favor those from privileged backgrounds.

The problem with the American education is not the lack of resources, but the unwillingness or inability (sometimes both) to apply them constructively. Here’s a list of suggestions that may help eradicate the plague of education inequality:

• Intent – The first step in bringing about change is to recognize the need for changes and then be willing to do what is necessary.
• Clear Objectives and Strategies – There needs to be a well-thought process of learning at the federal level. The policy makers need to understand why reforms are needed. It is only with this clarity, will they be able to set clear and achievable goals and be able to plot the best possible course towards achieving them. Inclusion of subjects such as Sex Education and Gender Studies are examples of some steps in the right direction.
• Standardization and Equalization – Standardization, such as of curriculum, testing, and facilities, and accountability – holding all schools and institutions to the same standards and regulations – can help bridge the gaps. The distribution of resources should be equitable, if not equal, and proportionate, across schools and institutions. However, standardization should also be applied at an individual level. Students should be held to the same standards and given the opportunities to enjoy the same advantages regardless of race, gender, economic status etc.
• Teachers and Teaching Methods – The role of teachers as leaders of changes in the education system should be recognized. Not only should they be involved in the process of reforming the system, but their value to the system needs to be appreciated. Most teachers today are overworked and underpaid. Some teachers are inexperienced or unqualified. Often they are found teaching subjects out of their zones of expertise. Also, Teachers need to be trained and retrained to stay on par with the latest teaching methods in the world. They should also be trained about subjects such as harassment and discrimination.
• Troubled Neighborhoods – There should be a special emphasis on schools in areas with high rates of crime. The schools should be safeguarded, however, I believe that a high quality education can be the biggest safeguard of all.
• Bring Parents on Board – While PTAs exist and play an important role in the education system, policy makers need to recognize the roles of parents as informal educations. This can be a significant contributor to the end of inequality in the American education system.

1 – https://digitalcommons.sacredheart.edu/cgi/viewcontent.cgi 2 – https://stars.library.ucf.edu/cgi/viewcontent.cgi 3 – https://www.usnews.com/news/blogs/data-mine/2015/01/28/us-education-still-separate-and-unequal 4 – https://www.dosomething.org/us/facts/11-facts-about-education-america 5 – https://www.brookings.edu/blog/brown-center-chalkboard/2018/04/23/how-our-education-system-undermines-gender-equity/ 6 – https://soeonline.american.edu/blog/reducing-inequality-in-the-us-education-system 7 – https://news.harvard.edu/gazette/story/2016/02/the-costs-of-inequality-educations-the-one-key-that-rules-them-all/

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Peer-reviewed

Research Article

Understanding the Influence of Race/Ethnicity, Gender, and Class on Inequalities in Academic and Non-Academic Outcomes among Eighth-Grade Students: Findings from an Intersectionality Approach

* E-mail: [email protected]

Affiliation Centre on Dynamics of Ethnicity, Department of Social Statistics, University of Manchester, Manchester, United Kingdom

Affiliation Australian National University, Acton, Australia

• Laia Bécares, 
• Naomi Priest

• Published: October 27, 2015
• https://doi.org/10.1371/journal.pone.0141363
• Reader Comments

Socioeconomic, racial/ethnic, and gender inequalities in academic achievement have been widely reported in the US, but how these three axes of inequality intersect to determine academic and non-academic outcomes among school-aged children is not well understood. Using data from the US Early Childhood Longitudinal Study—Kindergarten (ECLS-K; N = 10,115), we apply an intersectionality approach to examine inequalities across eighth-grade outcomes at the intersection of six racial/ethnic and gender groups (Latino girls and boys, Black girls and boys, and White girls and boys) and four classes of socioeconomic advantage/disadvantage. Results of mixture models show large inequalities in socioemotional outcomes (internalizing behavior, locus of control, and self-concept) across classes of advantage/disadvantage. Within classes of advantage/disadvantage, racial/ethnic and gender inequalities are predominantly found in the most advantaged class, where Black boys and girls, and Latina girls, underperform White boys in academic assessments, but not in socioemotional outcomes. In these latter outcomes, Black boys and girls perform better than White boys. Latino boys show small differences as compared to White boys, mainly in science assessments. The contrasting outcomes between racial/ethnic and gender minorities in self-assessment and socioemotional outcomes, as compared to standardized assessments, highlight the detrimental effect that intersecting racial/ethnic and gender discrimination have in patterning academic outcomes that predict success in adult life. Interventions to eliminate achievement gaps cannot fully succeed as long as social stratification caused by gender and racial discrimination is not addressed.

Citation: Bécares L, Priest N (2015) Understanding the Influence of Race/Ethnicity, Gender, and Class on Inequalities in Academic and Non-Academic Outcomes among Eighth-Grade Students: Findings from an Intersectionality Approach. PLoS ONE 10(10): e0141363. https://doi.org/10.1371/journal.pone.0141363

Editor: Emmanuel Manalo, Kyoto University, JAPAN

Received: June 10, 2015; Accepted: October 6, 2015; Published: October 27, 2015

Copyright: © 2015 Bécares, Priest. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All ECLS-K Kindergarten-Eighth Grade Public-use File are available from the National Center for Education Statistics website ( https://nces.ed.gov/ecls/dataproducts.asp#K-8 ).

Funding: This work was funded by an ESRC grant (ES/K001582/1) and a Hallsworth Research Fellowship to LB.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The US racial/ethnic academic achievement gap is a well-documented social inequality [ 1 ]. National assessments for science, mathematics, and reading show that White students score higher on average than all other racial/ethnic groups, particularly when compared to Black and Hispanic students [ 2 , 3 ]. Explanations for these gaps tend to focus on the influence of socioeconomic resources, neighborhood and school characteristics, and family composition in patterning socioeconomic inequalities, and on the racialized nature of socioeconomic inequalities as key drivers of racial/ethnic academic achievement gaps [ 4 – 10 ]. Substantial evidence documents that indicators of socioeconomic status, such as free or reduced-price school lunch, are highly predictive of academic outcomes [ 2 , 3 ]. However, the relative contribution of family, neighborhood and school level socioeconomic inequalities to racial/ethnic academic inequalities continues to be debated, with evidence suggesting none of these factors fully explain racial/ethnic academic achievement gaps, particularly as students move through elementary school [ 11 ]. Attitudinal outcomes have been proposed by some as one explanatory factor for racial/ethnic inequalities in academic achievement [ 12 ], but differences in educational attitudes and aspirations across groups do not fully reflect inequalities in academic assessment. For example, while students of poorer socioeconomic status have lower educational aspirations than more advantaged students [ 13 ], racial/ethnic minority students report higher educational aspirations than White students, particularly after accounting for socioeconomic characteristics [ 14 – 16 ]. Similarly, while socio-emotional development is considered highly predictive of academic achievement in school students, some racial/ethnic minority children report better socio-emotional outcomes than their White peers on some indicators, although findings are inconsistent [ 17 – 22 ].

In addition to inequalities in academic achievement, racial/ethnic and socioeconomic inequalities also exist across measures of socio-emotional development [ 23 – 26 ]. And as with academic achievement, although socioeconomic factors are highly predictive of socio-emotional outcomes, they do not completely explain racial/ethnic inequalities in school-related outcomes not focused on standardized assessments [ 11 ].

Further complexity in understanding how academic and non-academic outcomes are patterned by socioeconomic factors, and how this contributes to racial/ethnic inequalities, is added by the multi-dimensional nature of socioeconomic status. Socioeconomic status is widely recognized as comprising diverse factors that operate across different levels (e.g. individual, household, neighborhood), and influence outcomes through different causal pathways [ 27 ]. The lack of interchangeability between measures of socioeconomic status within and between levels (e.g. income, education, occupation, wealth, neighborhood socioeconomic characteristics, or past socioeconomic circumstances) is also well established, as is the non-equivalence of measures between racial/ethnic groups [ 27 ]. For example, large inequalities have been reported across racial/ethnic groups within the same educational level, and inequalities in wealth have been shown across racial/ethnic that have similar income. It is therefore imperative that studies consider these multiple dimensions of socioeconomic status so that critical social gradients across the entire socioeconomic spectrum are not missed [ 27 ], and racial/ethnic inequalities within levels of socioeconomic status are adequately documented. It is also important that differences in school outcomes are considered across levels of socioeconomic status within and between racial/ethnic groups, so that the influence of specific socioeconomic factors on outcomes within specific racial/ethnic groups can be studied [ 28 ]. However, while these analytic approaches have been identified as research priorities in order to enhance our understanding of the complex ways in which socioeconomic status and race/ethnicity intersect to influence school outcomes, research that operationalizes these recommendations across academic and non-academic outcomes of school children is scant.

In addition to the complexity that arises from race/ethnicity, socioeconomic status, and intersections between them, different patterns in academic and non-academic outcomes by gender have also received longstanding attention. Comparisons across gender show that, on average, boys have higher scores in mathematics and science, whereas girls have higher scores in reading [ 2 , 3 , 29 ]. In contrast to explanations for socioeconomic inequalities, gender differences have been mainly attributed to social conditioning and stereotyping within families, schools, communities, and the wider society [ 30 – 35 ]. These socialization and stereotyping processes are also highly relevant determining factors in explaining racial/ethnic academic and non-academic inequalities [ 35 , 36 ], as are processes of racial discrimination and stigmatization [ 37 , 38 ]. Gender differences in academic outcomes have been documented as differently patterned across racial/ethnic groups and across levels of socioeconomic status. For example, gender inequalities in math and science are largest among White and Latino students, and smallest among Asian American and African American students [ 39 – 43 ], while gender gaps in test scores are more pronounced among socioeconomically disadvantaged children [ 44 , 45 ]. In terms of attitudes towards math and sciences, gender differences in attitudes towards math are largest among Latino students, but gender differences in attitudes towards science are largest among White students [ 39 , 40 ]. Gender differences in socio-developmental outcomes and in non-cognitive academic outcomes, across race/ethnicity and socio-economic status, have received far less attention; studies that consider multiple academic and non-academic outcomes among school aged children across race/ethnicity, socioeconomic status and gender are limited in the US and internationally.

Understanding how different academic and non-academic outcomes are differently patterned by race/ethnicity, socio-economic status, and gender, including within and between group differences, is an important research area that may assist in understanding the potential causal pathways and explanations for observed inequalities, and in identifying key population groups and points at which interventions should be targeted to address inequalities in particular outcomes [ 28 , 46 ]. Not only is such knowledge critical for population level policy and/or local level action within affected communities, but failing to detect potential factors for interventions and potential solutions is argued as reinforcing perceptions of the unmodifiable nature of inequality and injustice [ 46 ].

Notwithstanding the importance of documenting patterns of inequality in relation to a particular social identity (e.g. race/ethnicity, gender, class), there is increasing acknowledgement within both theoretical and empirical research of the need to move beyond analyzing single categories to consider simultaneous interactions between different aspects of social identity, and the impact of systems and processes of oppression and domination (e.g., racism, classism, sexism) that operate at the micro and macro level [ 47 , 48 ]. Such intersectional approaches challenge practices that isolate and prioritize a single social position, and emphasize the potential of varied inter-relationships of social identities and interacting social processes in the production of inequities [ 49 – 51 ]. To date, exploration of how social identities interact in an intersectional way to influence outcomes has largely been theoretical and qualitative in nature. Explanations offered for interactions between privileged and marginalized identities, and associated outcomes, include family and teacher socialization of gender performance (e.g. math and science as male domains, verbal and emotional skills as female), as well as racialized stereotypes and expectations from teachers and wider society regarding racial/ethnic minorities that are also gendered (e.g. Black males as violent prone and aggressive, Asian females as submissive) [ 52 – 57 ]. That is, social processes that socialize and pattern opportunities and outcomes are both racialized and gendered, with racism and sexism operating in intersecting ways to influence the development and achievements of children and youth [ 58 – 60 ]. Socioeconomic status adds a third important dimension to these processes, with individuals of the same race/ethnicity and gender having access to vastly different resources and opportunities across levels of socioeconomic status. Moreover, access to resources as well as socialization experiences and expectations differ considerably by race and gender within the same level of socio-economic status. Thus, neither gender nor race nor socio-economic status alone can fully explain the interacting social processes influencing outcomes for youth [ 27 , 28 ]. Disentangling such interactions is therefore an important research priority in order to inform intervention to address inequalities at a population level and within local communities.

In the realm of quantitative approaches to the study of inequality, studies often examine separate social identities independently to assess which of these axes of stratification is most prominent, and for the most part do not consider claims that the varied dimensions of social stratification are often juxtaposed [ 56 , 61 ]. A pressing need remains for quantitative research to consider how multiple forms of social stratification are interrelated, and how they combine interactively, not just additively, to influence outcomes [ 46 ]. Doing so enables analyses that consider in greater detail the representation of the embodied positions of individuals, particularly issues of multiple marginalization as well as the co-occurrence of some form of privilege with marginalization [ 46 ]. It is important to note that the languages of statistical interaction and of intersectionality need to be carefully distinguished (e.g. intersectional additivity or additive assumptions, versus additive scale and cross-product interaction terms) to avoid misinterpretation of findings, and to ensure appropriate application of statistical interaction to enable the description of outcome measures for groups of individuals at each cross-stratified intersection [ 46 ]. Ultimately this will provide more nuanced and realistic understandings of the determinants of inequality in order to inform intervention strategies.

This study fills these gaps in the literature by examining inequalities across several eighth grade academic and non-academic outcomes at the intersection of race/ethnicity, gender, and socioeconomic status. It aims to do this by: identifying classes of socioeconomic advantage/disadvantage from kindergarten to eighth grade; then ascertaining whether membership into classes of socioeconomic advantage/disadvantage differ for racial/ethnic and gender groups; and finally, by contrasting academic and non-academic outcomes at the intersection of race/ethnicity, gender and socioeconomic advantage/disadvantage. Intersecting identities of race/ethnicity, gender, and socioeconomic characteristics are compared to the reference group of White boys in the most advantaged socioeconomic category, as these are the three identities (male, White, socioeconomically privileged) that experience the least marginalization when compared to racial/ethnic and gender minority groups in disadvantaged socioeconomic positions.

This study used data on singleton children from the Early Childhood Longitudinal Study—Kindergarten (ECLS-K). The ECLS-K employed a multistage probability sample design to select a nationally representative sample of children attending kindergarten in 1998–99. In the base year the primary sampling units (PSUs) were geographic areas consisting of counties or groups of counties. The second-stage units were schools within sampled PSUs. The third- and final-stage units were children within schools [ 62 ]. Analyses were conducted on data collected from direct child assessments, as well as information provided by parents and school administrators.

Ethics Statement

This article is based on the secondary analysis of anonymized and de-identified Public-Use Data Files available to researchers via the Inter-University Consortium for Political and Social Research (ICPSR). Human participants were not directly involved in the research reported in this article; therefore, no institutional review board approval was sought.

Outcome Variables.

Eight outcome variables, all assessed in eighth grade, were selected to examine the study aims: two measures relating to non-cognitive academic skills (perceived interest/competence in reading, and in math); three measures capturing socioemotional development (internalizing behavior, locus of control, self-concept); and three measures of cognitive skills (math, reading and science assessment scores).

For the eighth-grade data collection, children completed the 16-item Self Description Questionnaire (SDQ) II [ 63 ], where they provided self-assessments of their academic skills by rating their perceived competence and interest in English and mathematics. The SDQ also asked children to report on problem behaviors with which they might struggle. Three subscales were produced from the SDQ items: The SDQ Perceived Interest/Competence in Reading, including four items on grades in English and the child’s interest in and enjoyment of reading. The SDQ Perceived Interest/Competence in Math, including four items on mathematics grades and the child’s interest in and enjoyment of mathematics. And the SDQ Internalizing Behavior subscale, which includes eight items on internalizing problem behaviors such as feeling sad, lonely, ashamed of mistakes, frustrated, and worrying about school and friendships [ 62 ].

The Self-Concept and Locus of Control scales ask children about their self-perceptions and the amount of control they have over their own lives. These scales, adopted from the National Education Longitudinal Study of 1988, asked children to indicate the degree to which they agreed with 13 statements (seven items in the Self-Concept scale, and six items in the Locus of Control Scale) about themselves, including “I feel good about myself,” “I don’t have enough control over the direction my life is taking,” and “At times I think I am no good at all.” Responses ranged from “strongly agree” to “strongly disagree.” Some items were reversed coded so that higher scores indicate more positive self-concept and a greater perception of control over one’s own life. The seven items in the Self-Concept scale, and the six items in the Locus of Control were standardized separately to a mean of zero and a standard deviation of 1. The scores of each scale are an average of the standardized scores [ 62 ].

Academic achievement in reading, mathematics and science was measured with the eighth-grade direct cognitive assessment battery [ 62 ].

Children were given separate routing assessment forms to determine the level (high/low) of their reading, mathematics, and science assessments. The two-stage cognitive assessment approach was used to maximize the accuracy of measurement and reduce administration time by using the child’s responses from a brief first-stage routing form to select the appropriate second-stage level form. First, children read items in a booklet and recorded their responses on an answer form. These answer forms were then scored by the test administrator. Based on the score of the respective routing forms, the test administrator then assigned a high or low second-stage level form of the reading and mathematics assessments. For the second-stage level tests, children read items in the assessment booklet and recorded their responses in the same assessment booklet. The routing tests and the second-stage tests were timed for 80 minutes [ 62 ]. The present analyses use the standardized scores (T-scores), allowing relative comparisons of children against their peers.

Individual and Contextual Disadvantage Variables.

Latent Class Analysis, described in greater detail below, was used to classify students into classes of individual and contextual advantage or disadvantage. Nine constructs, measuring characteristics at the individual-, school-, and neighborhood-level, were captured using 42 dichotomous variables measured across the different waves of the ECLS-K.

Individual-level variables captured household composition, material disadvantage, and parental expectations of the children’s success. Measures included whether the child lived in a single-parent household at kindergarten, first, third, fifth and eighth grades; whether the household was below the poverty threshold level at kindergarten, fifth and eighth grades; food insecurity at kindergarten, first, second and third grades; and parental expectations of the child’s academic achievement (categorized as up to high school and more than high school) at kindergarten, first, third, fifth and eighth grades. An indicator of whether parents had moved since the previous interview (measured at kindergarten, first, third, fifth and eighth grades) was included to capture stability in the children’s life. A household-level composite index of socioeconomic status, derived by the National Center for Education Statistics, was also included at kindergarten, first, third, fifth and eighth grades. This measure captured the father/male guardian’s education and occupation, the mother/female guardian’s education and occupation, and the household income. Higher scores reflect higher levels of educational attainment, occupational prestige, and income. In the present analyses, the socioeconomic composite index was categorized into quintiles and further divided into the lowest first and second quintiles, versus the third, fourth and fifth quintiles.

Two variables measured the school-level environment: percentage of students eligible for free school meals, and percentage of students from a racial/ethnic background other than White non-Hispanic. These two variables were dichotomized as more than or equal to 50% of students belonging to each category. Both variables were measured in the kindergarten, first, third, fifth and eighth grade data collections.

To capture the neighborhood environment, a variable was included which measured the level of safety of the neighborhood in kindergarten, first, third, fifth and eighth grades. Parents were asked “How safe is it for children to play outside during the day in your neighborhood?” with responses ranging from 1, not at all safe, to 3, very safe. For the present analyses, response categories were recoded into 1 “not at all and somewhat safe,” and 0 “very safe.”

Predictor Variables.

The race/ethnicity and gender of the children were assessed during the parent interview. In order to empirically measure the intersection between race/ethnicity and gender in the classes of disadvantage, a set of six dummy variables were created that combined racial/ethnic and gender categories into White boys, White girls, Black boys, Black girls, Latino boys, and Latina girls.

Statistical Analyses

This study used the manual 3-step approach in mixture modeling with auxiliary variables [ 64 , 65 ] to independently evaluate the relationship between the predictor auxiliary variables (the combined race/ethnicity and gender groups), the latent class variable of advantage/disadvantage, and the outcome (non-cognitive skills, socioemotional development, cognitive assessments). This is a data-driven, mixture modelling technique which uses indicator variables (in this case the variables described under Individual and Contextual Disadvantage Variables section) to identify a number of latent classes. It also includes auxiliary information in the form of covariates (the race/ethnicity and gender combinations described under Predictor Variables) and distal outcomes (the eight outcome variables), to better explore the relationships between the characteristics that make up the latent classes, the predictors of class membership, and the associated consequences of membership into each class.

The first step in the 3-step procedure is to estimate the measurement part of the joint model (i.e., the latent class model) by creating the latent classes without adding covariates. Latent class analyses first evaluated the fit of a 2-class model, and systematically increased the number of classes in subsequent models until the addition of latent classes did not further improve model fit. For each model, replication of the best log-likelihood was verified to avoid local maxima. To determine the optimal number of classes, models were compared across several model fit criteria. First, the sample-size adjusted Bayesian Information Criterion (BIC) [ 66 ] was evaluated; lower relative BIC values indicate improved model fit. Given that the BIC criterion tends to favor models with fewer latent classes [ 67 ], the Lo, Mendell, and Rubin likelihood ratio test (LMR-LRT) statistic [ 68 ] was also considered. The LMR-LRT can be used in mixture modeling to compare the fit of the specified class solution ( k -class model) to a model with fewer classes ( k -1 class model). A non-significant chi-square value suggests that a model with one fewer class is preferred. Entropy statistics, which measure the separation of the classes based on the posterior class membership probabilities, were also examined; entropy values approaching 1 indicate clear separation between classes [ 69 ].

After determining the latent class model in step 1, the second step of the analyses used the latent class posterior distribution to generate a nominal variable N , which represented the most likely class [ 64 ]. During the third step, the measurement error for N was accounted for while the model was estimated with the outcomes and predictor auxiliary variables [ 64 ]. The last step of the analysis examined whether race/ethnic and gender categories predict class membership, and whether class membership predicts the outcomes of interest.

All analyses were conducted using MPlus v. 7.11 [ 70 ], and used longitudinal weights to account for differential probabilities of selection at each sampling stage and to adjust for the effects of non-response. A robust standard error estimator was used in MPlus to account for the clustering of observations in the ECLS-K.

Four distinct classes of advantage/disadvantage were identified in the latent class analysis (see Table 1 ).

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https://doi.org/10.1371/journal.pone.0141363.t001

Class characteristics are shown in Table A in S1 File . Trajectories of advantage and disadvantage were stable across ECLS-K waves, so that none of the classes identified changed in individual and contextual characteristics across time. The largest proportion of the sample (47%; Class 3: Individually and Contextually Wealthy) lived in individual and contextual privilege, with very low proportions of children in socioeconomic deprived contexts. A class representing the opposite characteristics (children living in individually- and contextually-deprived circumstances) was also identified in the analyses (19%; Class 1: Individually and Contextually Disadvantaged). Class 1 had the highest proportion of children living in socioeconomic deprivation, attending schools with more than 50% racial/ethnic minority students, and living in unsafe neighborhoods, but did not have a high proportion of children with the lowest parental expectations. Class 4 (19%; Individually Disadvantaged, Contextually Wealthy) had the highest proportion of children with the lowest parental expectations (parents reporting across waves that they expected children to achieve up to a high school education). Class 4 (Individually Disadvantaged, Contextually Wealthy) also had high proportions of children living in individual-level socioeconomic deprivation, but had low proportions of children attending a school with over 50% of children eligible for free school meals. It also had relatively low proportions of children living in unsafe neighborhoods and low proportions of children attending diverse schools, forming a class with a mixture of individual-level deprivation, and contextual-level advantage. The last class was composed of children who lived in individually-wealthy environments, but who also lived in unsafe neighborhoods and attended diverse schools where more than 50% of pupils were eligible for free school meals (13%; Class 2: Individually Wealthy, Contextually Disadvantaged; see Table A in S1 File ).

The combined intersecting racial/ethnic and gender characteristics yielded six groups consisting of White boys (n = 2998), White girls (n = 2899), Black boys (n = 553), Black girls (n = 560), Latino boys (n = 961), and Latina girls (n = 949). All pairs containing at least one minority status of either race/ethnicity or gender (e.g., Black boys, Black girls, Latino boys, Latina girls) were more likely than White boys to be assigned to the more disadvantaged classes, as compared to being assigned to Class 3, the least disadvantaged (see Table B in S1 File ).

Racial/Ethnic and Gender Differences in Eighth-Grade Academic Outcomes

Table 2 shows broad patterns of intersecting racial/ethnic and gender inequalities in academic outcomes, although interesting differences emerge across racial/ethnic and gender groups. Whereas Black boys achieved lower scores than White boys across all classes on the math, reading and science assessments, this was not the case for Latino boys, who only underperformed White boys on the science assessment within the most privileged class (Class 3: Individually and Contextually Wealthy). Latina girls, in contrast, outperformed White boys on reading scores within Class 4 (Individually Disadvantaged, Contextually Wealthy), but scored lower than White boys on science and math assessments, although only when in the two most privileged classes (Class 3 and 4). For Black girls the effect of class membership was not as pronounced, and they had lower science and math scores than White boys across all but one instance.

https://doi.org/10.1371/journal.pone.0141363.t002

In general, the largest inequalities in academic outcomes across racial/ethnic and gender groups appeared in the most privileged classes. For example, results show no differences in math scores across racial/ethnic and gender categories within Class 4, the most disadvantaged class, but in all other classes that contain an element of advantage, and particularly in Class 3 (Individually and Contextually Wealthy), there are large gaps in math scores across racial/ethnic and gender groups, when compared to White boys. These patterns of heightened inequality in the most advantaged classes are similar for reading and science scores (see Table 2 ).

Racial/Ethnic and Gender Differences in Eighth-Grade Non-Academic Outcomes

Interestingly, racialized and gendered patterns of inequality observed in academic outcomes were not as stark in non-cognitive academic outcomes (see Table 3 ).

https://doi.org/10.1371/journal.pone.0141363.t003

Racial/ethnic and gender differences were small across socioemotional outcomes, and in fact, White boys were outperformed on several outcomes. Black boys scored lower than White boys on internalizing behavior and higher on self-concept within Classes 2 (Individually Wealthy, Contextually Disadvantaged) and 4 (Individually Disadvantaged, Contextually Wealthy), and Black girls scored higher than White boys on self-concept within Classes 2 and 3 (Individually Wealthy, Contextually Disadvantaged, and Individually and Contextually Wealthy, respectively). White and Latina girls, but not Black girls, scored higher than White boys on internalizing behavior (within Classes 3 and 4 for White girls, and within Classes 1 and 3 for Latina girls; see Table 3 ).

As with academic outcomes, most racial/ethnic and gender differences also emerged within the most privileged classes, and particularly in Class 3 (Individually and Contextually Wealthy), although in the case of perceived interest/competence in reading, White and Latina girls performed better than White boys. White girls also reported higher perceived interest/competence in reading than White boys in Class 4: Individually Disadvantaged, Contextually Wealthy.

This study set out to examine inequalities across several eighth grade academic and non-academic outcomes at the intersection of race/ethnicity, gender, and socioeconomic status. It first identified four classes of longstanding individual- and contextual-level disadvantage; then determined membership to these classes depending on racial/ethnic and gender groups; and finally compared non-cognitive skills, academic assessment scores, and socioemotional outcomes across intersecting gender, racial/ethnic and socioeconomic social positions.

Results show the clear influence of race/ethnicity in determining membership to the most disadvantaged classes. Across gender dichotomies, Black students were more likely than White boys to be assigned to all classes of disadvantage as compared to the most advantaged class, and this was particularly strong for the most disadvantaged class, which included elements of both individual- and contextual-level disadvantage. Latino boys and girls were also more likely than White boys to be assigned to all the disadvantaged classes, but the strength of the association was much smaller than for Black students. Whereas membership into classes of disadvantage appears to be more a result of structural inequalities strongly driven by race/ethnicity, the salience of gender is apparent in the distribution of academic assessment outcomes within classes of disadvantage. Results show a gendered pattern of math, reading and science assessments, particularly in the most privileged class, where girls from all ethnic/racial groups (although mostly from Black and Latino racial/ethnic groups) underperform White boys in math and science, and where Black boys score lower, and White girls higher, than White boys in reading.

With the exception of educational assessments, gender and racial/ethnic inequalities within classes are either not very pronounced or in the opposite direction (e.g. racial/ethnic and gender minorities outperform White males), but differences in outcomes across classes are stark. The strength of the association between race/ethnicity and class membership, and the reduced racial/ethnic and gender inequalities within classes of advantage and disadvantage, attest to the importance of socioeconomic status and wealth in explaining racial/ethnic inequalities; should individual and contextual disadvantage be comparable across racial/ethnic groups, racial/ethnic inequalities would be substantially reduced. This being said, most within-class differences were observed in the most privileged classes, showing that benefits brought about by affluence and advantage are not equal across racial/ethnic and gender groups. The measures of advantage and disadvantage captured in this study relate to characteristics afforded by parental resources, implying an intergenerational transmission of disadvantage, regardless of the presence of absolute adversity in childhood. This pattern of differential returns of affluence has been shown in other studies, which report that White teenagers benefit more from the presence of affluent neighbors than do Black teenagers [ 71 ]. Among adult populations, studies show that across several health outcomes, highly educated Black adults fare worse than White adults with the lowest education [ 72 ]. Intersectional approaches such as the one applied in this study reveal how power within gendered and racialized institutional settings operates to undermine access to and use of resources that would otherwise be available to individuals of advantaged classes [ 72 ]. The present study further contributes to this literature by documenting how, in a key stage of the life course, similar levels of advantage, but not disadvantage, lead to different academic outcomes across racial/ethnic and gender groups. These findings suggest that, should socioeconomic inequalities be addressed, and levels of advantage were similar across racial/ethnic and gender groups, systems of oppression that pattern the racialization and socialization of children into racial/ethnic and gender roles in society would still ensure that inequalities in academic outcomes existed across racial/ethnic and gender categories. In other words, racism and sexism have a direct effect on academic and non-academic outcomes among 8 th graders, independent of the effect of socioeconomic disadvantage on these outcomes. An important limitation of the current study is that although it uses a comprehensive measure of advantage/disadvantage, including elements of deprivation and affluence at the family, school and neighborhood levels through time, it failed to capture these two key causal determinants of racial/ethnic and gender inequality: experiences of racial and gender discrimination.

Despite this limitation, it is important to note that socioeconomic inequalities in the US are driven by racial and gender bias and discrimination at structural and individual levels, with race and gender discrimination exerting a strong influence on academic and non-academic inequalities. Racial discrimination, prevalent in the US and in other industrialized nations [ 38 , 73 ] determines differential life opportunities and resources across racial/ethnic groups, and is a crucial determinant of racial/ethnic inequalities in health and development throughout life and across generations [ 37 , 38 ]. In the context of this study’s primary outcomes within school settings, racism and racial discrimination experienced by both the parents and the children are likely to contribute towards explaining observed racial/ethnic inequalities in outcomes within classes of disadvantage. Gender discrimination—another system of oppression—is apparent in this study in relation to academic subjects socially considered as typically male or female orientated. For example, results show no difference between Black girls and White boys from the most advantaged class in terms of perceived interest and competence in math but, in this same class, Black girls score much lower than White boys in the math assessment. This difference, not explained by intrinsic or socioeconomic differences, can be contextualized as a consequence of experienced intersecting racial and gender discrimination. The consequences of the intersection between two marginalized identities are found throughout the results of this study when comparing across broad categorizations of race/ethnicity and gender, and in more detailed conceptualizations of minority status. Growing up Black, Latino or White in the US is not the same for boys and girls, and growing up as a boy or a girl in America does not lead to the same outcomes and opportunities for Black, Latino and White children as they become adults. With this study’s approach of intersectionality one can observe the complexity of how gender and race/ethnicity intersect to create unique academic and non-academic outcomes. This includes the contrasting results found for Black and Latino boys, when compared to White boys, which show very few examples of poorer outcomes among Latino boys, but several instances among Black boys. Results also show different racialization for Black and Latina girls. Latina girls, but not Black girls, report higher internalizing behavior than White boys, whereas Black girls, but not Latina girls, report higher self-concept than White boys. Black boys also report higher self-concept and lower internalizing behavior than White boys, findings that mirror research on self-esteem among Black adolescents [ 74 , 75 ]. In cognitive assessments, intersecting racial/ethnic and gender differences emerge across classes of disadvantage. For example, Black girls in all four classes score lower on science scores than White boys, but only Latina girls in the most advantaged class score lower than White boys. Although one can observe differences in the racialization of Black and Latino boys and girls across classes of disadvantage, findings about broad differences across Latino children compared to Black and White children should be interpreted with caution. The Latino ethnic group is a large, heterogeneous group, representing 16.7% of the total US population [ 76 ]. The Latino population is composed of a variety of different sub-groups with diverse national origins and migration histories [ 77 ], which has led to differences in sociodemographic characteristics and lived experiences of ethnicity and minority status among the various groups. Differences across Latino sub-groups are widely documented, and pooled analyses such as those reported here are masking differences across Latino sub-groups, and providing biased comparisons between Latino children, and Black and White children.

Poorer performance of girls and racial/ethnic minority students in science and math assessments (but not in self-perceived competence and interest) might result from stereotype threat, whereby negative stereotypes of a group influence their member’s performance [ 78 ]. Stereotype threat posits that awareness of a social stereotype that reflects negatively on one's social group can negatively affect the performance of group members [ 35 ]. Reduced performance only occurs in a threatening situation (e.g., a test) where individuals are aware of the stereotype. Studies show that early adolescence is a time when youth become aware of and begin to endorse traditional gender and racial/ethnic stereotypes [ 79 ]. Findings among youth parallel findings among adult populations, which show that adult men are generally perceived to be more competent than women, but that these perceptions do not necessarily hold for Black men [ 80 ]. These stereotypes have strong implications for interpersonal interactions and for the wider structuring of systemic racial/ethnic and gender inequalities. An example of the consequences of negative racial/ethnic and gender stereotypes as children grow up is the well-documented racial/ethnic and gender pay gap: women earn less than men [ 81 ], and racial/ethnic minority women and men earn less than White men [ 82 ].

In addition to the focus on intersectionality, a strength of this study is its person-centered methodological approach, which incorporates measures of advantage and disadvantage across individual and contextual levels through nine years of children’s socialization. Children live within multiple contexts, with risk factors at the family, school, and neighborhood level contributing to their development and wellbeing. Individual risk factors seldom operate in isolation [ 83 ], and they are often strongly associated both within and across levels [ 84 ]. All risk factors captured in the latent class analyses have been independently associated with increased risk for academic problems [ 10 , 71 , 85 , 86 ], and given that combinations of risk factors that cut across multiple domains explain the association between early risk and later outcomes better than any isolated risk factor [ 83 , 84 ], the incorporation of person-centered and intersectionality approaches to the study of racial/ethnic, gender, and socioeconomic inequalities across school outcomes provides new insight into how children in marginalized social groups are socialized in the early life course.

Conclusions

The contrasting outcomes between racial/ethnic and gender minorities in self-assessment and socioemotional outcomes, as compared to standardized assessments, provide support for the detrimental effect that intersecting racial/ethnic and gender discrimination have in patterning academic outcomes that predict success in adult life. Interventions to eliminate achievement gaps cannot fully succeed as long as social stratification caused by gender and racial discrimination is not addressed [ 87 , 88 ].

Supporting Information

S1 file. supporting tables..

Table A: Class characteristics. Table B: Associations between race/ethnicity and gender groups and assigned class membership (membership to Classes 1, 2 or 4 as compared to Class 3: Individually and Contextually Wealthy).

https://doi.org/10.1371/journal.pone.0141363.s001

Acknowledgments

This work was funded by an ESRC grant (ES/K001582/1) and a Hallsworth Research Fellowship to LB. Most of this work was conducted while LB was a visiting scholar at the Institute for Social Research, University of Michigan. She would like to thank them for hosting her visit and for the support provided.

Author Contributions

Conceived and designed the experiments: LB. Performed the experiments: LB. Analyzed the data: LB. Wrote the paper: LB NP.

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How COVID taught America about inequity in education

Harvard Correspondent

Remote learning turned spotlight on gaps in resources, funding, and tech — but also offered hints on reform

“Unequal” is a multipart series highlighting the work of Harvard faculty, staff, students, alumni, and researchers on issues of race and inequality across the U.S. This part looks at how the pandemic called attention to issues surrounding the racial achievement gap in America.

The pandemic has disrupted education nationwide, turning a spotlight on existing racial and economic disparities, and creating the potential for a lost generation. Even before the outbreak, students in vulnerable communities — particularly predominately Black, Indigenous, and other majority-minority areas — were already facing inequality in everything from resources (ranging from books to counselors) to student-teacher ratios and extracurriculars.

The additional stressors of systemic racism and the trauma induced by poverty and violence, both cited as aggravating health and wellness as at a Weatherhead Institute panel , pose serious obstacles to learning as well. “Before the pandemic, children and families who are marginalized were living under such challenging conditions that it made it difficult for them to get a high-quality education,” said Paul Reville, founder and director of the Education Redesign Lab at the Harvard Graduate School of Education (GSE).

Educators hope that the may triggers a broader conversation about reform and renewed efforts to narrow the longstanding racial achievement gap. They say that research shows virtually all of the nation’s schoolchildren have fallen behind, with students of color having lost the most ground, particularly in math. They also note that the full-time reopening of schools presents opportunities to introduce changes and that some of the lessons from remote learning, particularly in the area of technology, can be put to use to help students catch up from the pandemic as well as to begin to level the playing field.

The disparities laid bare by the COVID-19 outbreak became apparent from the first shutdowns. “The good news, of course, is that many schools were very fast in finding all kinds of ways to try to reach kids,” said Fernando M. Reimers , Ford Foundation Professor of the Practice in International Education and director of GSE’s Global Education Innovation Initiative and International Education Policy Program. He cautioned, however, that “those arrangements don’t begin to compare with what we’re able to do when kids could come to school, and they are particularly deficient at reaching the most vulnerable kids.” In addition, it turned out that many students simply lacked access.

“We’re beginning to understand that technology is a basic right. You cannot participate in society in the 21st century without access to it,” says Fernando Reimers of the Graduate School of Education.

Stephanie Mitchell/Harvard file photo

The rate of limited digital access for households was at 42 percent during last spring’s shutdowns, before drifting down to about 31 percent this fall, suggesting that school districts improved their adaptation to remote learning, according to an analysis by the UCLA Center for Neighborhood Knowledge of U.S. Census data. (Indeed, Education Week and other sources reported that school districts around the nation rushed to hand out millions of laptops, tablets, and Chromebooks in the months after going remote.)

The report also makes clear the degree of racial and economic digital inequality. Black and Hispanic households with school-aged children were 1.3 to 1.4 times as likely as white ones to face limited access to computers and the internet, and more than two in five low-income households had only limited access. It’s a problem that could have far-reaching consequences given that young students of color are much more likely to live in remote-only districts.

“We’re beginning to understand that technology is a basic right,” said Reimers. “You cannot participate in society in the 21st century without access to it.” Too many students, he said, “have no connectivity. They have no devices, or they have no home circumstances that provide them support.”

The issues extend beyond the technology. “There is something wonderful in being in contact with other humans, having a human who tells you, ‘It’s great to see you. How are things going at home?’” Reimers said. “I’ve done 35 case studies of innovative practices around the world. They all prioritize social, emotional well-being. Checking in with the kids. Making sure there is a touchpoint every day between a teacher and a student.”

The difference, said Reville, is apparent when comparing students from different economic circumstances. Students whose parents “could afford to hire a tutor … can compensate,” he said. “Those kids are going to do pretty well at keeping up. Whereas, if you’re in a single-parent family and mom is working two or three jobs to put food on the table, she can’t be home. It’s impossible for her to keep up and keep her kids connected.

“If you lose the connection, you lose the kid.”

“COVID just revealed how serious those inequities are,” said GSE Dean Bridget Long , the Saris Professor of Education and Economics. “It has disproportionately hurt low-income students, students with special needs, and school systems that are under-resourced.”

This disruption carries throughout the education process, from elementary school students (some of whom have simply stopped logging on to their online classes) through declining participation in higher education. Community colleges, for example, have “traditionally been a gateway for low-income students” into the professional classes, said Long, whose research focuses on issues of affordability and access. “COVID has just made all of those issues 10 times worse,” she said. “That’s where enrollment has fallen the most.”

In addition to highlighting such disparities, these losses underline a structural issue in public education. Many schools are under-resourced, and the major reason involves sources of school funding. A 2019 study found that predominantly white districts got $23 billion more than their non-white counterparts serving about the same number of students. The discrepancy is because property taxes are the primary source of funding for schools, and white districts tend to be wealthier than those of color.

The problem of resources extends beyond teachers, aides, equipment, and supplies, as schools have been tasked with an increasing number of responsibilities, from the basics of education to feeding and caring for the mental health of both students and their families.

“You think about schools and academics, but what COVID really made clear was that schools do so much more than that,” said Long. A child’s school, she stressed “is social, emotional support. It’s safety. It’s the food system. It is health care.”

“You think about schools and academics” … but a child’s school “is social, emotional support. It’s safety. It’s the food system. It is health care,” stressed GSE Dean Bridget Long.

Rose Lincoln/Harvard file photo

This safety net has been shredded just as more students need it. “We have 400,000 deaths and those are disproportionately affecting communities of color,” said Long. “So you can imagine the kids that are in those households. Are they able to come to school and learn when they’re dealing with this trauma?”

The damage is felt by the whole families. In an upcoming paper, focusing on parents of children ages 5 to 7, Cindy H. Liu, director of Harvard Medical School’s Developmental Risk and Cultural Disparities Laboratory , looks at the effects of COVID-related stress on parent’ mental health. This stress — from both health risks and grief — “likely has ramifications for those groups who are disadvantaged, particularly in getting support, as it exacerbates existing disparities in obtaining resources,” she said via email. “The unfortunate reality is that the pandemic is limiting the tangible supports [like childcare] that parents might actually need.”

Educators are overwhelmed as well. “Teachers are doing a phenomenal job connecting with students,” Long said about their performance online. “But they’ve lost the whole system — access to counselors, access to additional staff members and support. They’ve lost access to information. One clue is that the reporting of child abuse going down. It’s not that we think that child abuse is actually going down, but because you don’t have a set of adults watching and being with kids, it’s not being reported.”

The repercussions are chilling. “As we resume in-person education on a normal basis, we’re dealing with enormous gaps,” said Reville. “Some kids will come back with such educational deficits that unless their schools have a very well thought-out and effective program to help them catch up, they will never catch up. They may actually drop out of school. The immediate consequences of learning loss and disengagement are going to be a generation of people who will be less educated.”

There is hope, however. Just as the lockdown forced teachers to improvise, accelerating forms of online learning, so too may the recovery offer options for educational reform.

The solutions, say Reville, “are going to come from our community. This is a civic problem.” He applauded one example, the Somerville, Mass., public library program of outdoor Wi-Fi “pop ups,” which allow 24/7 access either through their own or library Chromebooks. “That’s the kind of imagination we need,” he said.

On a national level, he points to the creation of so-called “Children’s Cabinets.” Already in place in 30 states, these nonpartisan groups bring together leaders at the city, town, and state levels to address children’s needs through schools, libraries, and health centers. A July 2019 “ Children’s Cabinet Toolkit ” on the Education Redesign Lab site offers guidance for communities looking to form their own, with sample mission statements from Denver, Minneapolis, and Fairfax, Va.

Already the Education Redesign Lab is working on even more wide-reaching approaches. In Tennessee, for example, the Metro Nashville Public Schools has launched an innovative program, designed to provide each student with a personalized education plan. By pairing these students with school “navigators” — including teachers, librarians, and instructional coaches — the program aims to address each student’s particular needs.

“This is a chance to change the system,” said Reville. “By and large, our school systems are organized around a factory model, a one-size-fits-all approach. That wasn’t working very well before, and it’s working less well now.”

“Students have different needs,” agreed Long. “We just have to get a better understanding of what we need to prioritize and where students are” in all aspects of their home and school lives.

“By and large, our school systems are organized around a factory model, a one-size-fits-all approach. That wasn’t working very well before, and it’s working less well now,” says Paul Reville of the GSE.

Already, educators are discussing possible responses. Long and GSE helped create The Principals’ Network as one forum for sharing ideas, for example. With about 1,000 members, and multiple subgroups to address shared community issues, some viable answers have begun to emerge.

“We are going to need to expand learning time,” said Long. Some school systems, notably Texas’, already have begun discussing extending the school year, she said. In addition, Long, an internationally recognized economist who is a member of the  National Academy of Education and the  MDRC board, noted that educators are exploring innovative ways to utilize new tools like Zoom, even when classrooms reopen.

“This is an area where technology can help supplement what students are learning, giving them extra time — learning time, even tutoring time,” Long said.

Reimers, who serves on the UNESCO Commission on the Future of Education, has been brainstorming solutions that can be applied both here and abroad. These include urging wealthier countries to forgive loans, so that poorer countries do not have to cut back on basics such as education, and urging all countries to keep education a priority. The commission and its members are also helping to identify good practices and share them — globally.

Innovative uses of existing technology can also reach beyond traditional schooling. Reimers cites the work of a few former students who, working with Harvard Global Education Innovation Initiative,   HundrED , the  OECD Directorate for Education and Skills , and the  World Bank Group Education Global Practice, focused on podcasts to reach poor students in Colombia.

They began airing their math and Spanish lessons via the WhatsApp app, which was widely accessible. “They were so humorous that within a week, everyone was listening,” said Reimers. Soon, radio stations and other platforms began airing the 10-minute lessons, reaching not only children who were not in school, but also their adult relatives.

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Racial Inequality in Education

Race, Eth­nic­i­ty and Education

The U.S. edu­ca­tion land­scape has long been a source of unequal treat­ment, access and out­comes based on a student’s race or ethnicity.

Black stu­dents, for exam­ple, are twice as like­ly as their white peers to be in inad­e­quate­ly fund­ed school dis­tricts and 3 . 5 times more like­ly to be in ​ “ chron­i­cal­ly under­fund­ed” dis­tricts , accord­ing to a  2024 report released by the Albert Shanker Insti­tute. The dis­crep­an­cies in fund­ing between His­pan­ic and white stu­dents are mod­er­ate­ly small­er but still large.

Some exam­ples of edu­ca­tion inequal­i­ty through­out his­to­ry include: 

• Between 1740 and 1867 : Anti-lit­er­a­cy laws pro­hib­it­ed enslaved, and some­times free, Black Amer­i­cans from learn­ing to read or write .
• 1954 : The U.S. Supreme Court famous­ly declared that ​ “ sep­a­rate is not equal” in a rul­ing that aimed to end the prac­tice of race-seg­re­gat­ed pub­lic schools. Although this case, known as Brown v. Board of Edu­ca­tion, end­ed legal seg­re­ga­tion in pub­lic schools, it did not end racial inequal­i­ty in edu­ca­tion .
• 1964 : The U.S. Supreme Court passed Title VI of the Civ­il Rights Act, which ruled that schools receiv­ing fed­er­al fund­ing could not dis­crim­i­nate stu­dents accord­ing to their race.

Though 70  years have passed since the U.S. Supreme Court pro­hib­it­ed seg­re­ga­tion, many of America’s pub­lic schools are still racial­ly and eth­ni­cal­ly iso­lat­ed . For instance: Among pub­lic schools nation­wide, 60 % of His­pan­ic stu­dents, 59 % of Black stu­dents and 54 % of Pacif­ic Islander stu­dents attend­ed schools where over 75 % of their class­mates shared their race or ethnicity.

At the same time, white stu­dents were most like­ly to attend schools where less than 25 % of their class­mates were stu­dents of col­or, accord­ing to fed­er­al data pre­sent­ed by the U.S. Sec­re­tary of Education.

The U.S. edu­ca­tion sys­tem con­tin­ues to nav­i­gate race-relat­ed issues and changes. Some of the most recent exam­ples of this include:

• Since Jan­u­ary 2021 : Leg­is­la­tors in 44 states have intro­duced bills ban­ning the teach­ing of race in pub­lic school class­rooms and 18 states have adopt­ed such legislation.
• In June 2023 : The U.S. Supreme Court dis­man­tled race-con­scious col­lege admis­sion poli­cies .

How Does Race Affect Edu­ca­tion Opportunities?

Not all pub­lic-school sys­tems and dis­tricts are equal, and these dif­fer­ences often fuel dif­fer­ent out­comes, oppor­tu­ni­ties and access to resources. This uneven land­scape con­tin­ues to fuel racial dis­par­i­ties that neg­a­tive­ly impact stu­dents of col­or. Some recent sta­tis­tics relat­ed to this real­i­ty include:

• Stu­dents of col­or fall short of read­ing and math pro­fi­cien­cy bench­marks at greater rates than their white peers, accord­ing to 2022 data report­ed by the KIDS COUNT ® Data Center.
• Among fourth graders nation­wide, 84 % of Black stu­dents, 82 % of Amer­i­can Indi­an stu­dents and 80 % of His­pan­ic stu­dents did not read at a fourth-grade pro­fi­cien­cy lev­el . A small­er share of their Asian/​Pacific Islander ( 45 %) and white ( 59 %) class­mates scored below 
• Among eighth graders nation­wide: 91 % of Black stu­dents, 89 % of Amer­i­can Indi­an stu­dents and 86 % of His­pan­ic stu­dents test­ed below pro­fi­cient in math . A small­er share of Asian /​Pacific Islander ( 44 %) and white ( 66 %) class­mates scored below pro­fi­cient in math.
• Black stu­dents are more like­ly to be dis­ci­plined in school when com­pared to their pub­lic-school peers of oth­er racial or eth­nic groups, notes the U.S. Gov­ern­ment Account­abil­i­ty Office. This real­i­ty is reflect­ed in the lat­est data report­ed by the KIDS COUNT Data Cen­ter, which cov­ers the 2017 – 2018 school year:
• Among all pub­lic-school stu­dents expelled from school, Black stu­dents were the race most like­ly to be expelled ( 49  in every 10 , 000 stu­dents) and Asian stu­dents were the least like­ly ( 4  in every 10 , 000 students).
• Among pub­lic school stu­dents issued out-of-school sus­pen­sions, Black stu­dents were most like­ly to be sus­pend­ed ( 12 %) fol­lowed by Amer­i­can Indi­an stu­dents ( 7 %). Asian stu­dents ( 1 %) as well as white and His­pan­ic stu­dents (both 4 %) were far less like­ly to suf­fer this same punishment.

Sus­pen­sion can dou­ble the risk of some­one drop­ping out of school , which — in turn — triples the risk of jus­tice involve­ment. Out­comes are worse in schools with a police pres­ence , which caus­es the fre­quen­cy of arrests for dis­or­der­ly con­duct to jump fivefold.

Grad­u­at­ing high school is, in many instances, a base­line require­ment for seek­ing employ­ment, advanced train­ing or a post­sec­ondary edu­ca­tion. In the 2021 – 2022 school year, the U.S. aver­age adjust­ed cohort grad­u­a­tion rate for pub­lic high school stu­dents was 87 %, per the Nation­al Cen­ter for Edu­ca­tion Sta­tis­tics . With­in this group, Asian/​Pacific Islander ( 94 %) and White ( 90 %) stu­dents were most like­ly to grad­u­ate high school where­as His­pan­ic ( 83 %), Black ( 81 %) and Amer­i­can Indian/​Alaska Native ( 74 %) stu­dents grad­u­at­ed at rates below the nation­al average.

Fac­tors in Racial Edu­ca­tion Gaps

Socioe­co­nom­ic sta­tus is a mul­ti-dimen­sion­al con­struct typ­i­cal­ly mea­sured by fam­i­ly income, par­ents’ occu­pa­tions and edu­ca­tion­al levels.

A poten­tial expla­na­tion for racial achieve­ment gaps is that they are large­ly due to socioe­co­nom­ic dis­par­i­ties between white, black, and His­pan­ic fam­i­lies . As Stan­ford University’s Cen­ter for Edu­ca­tion Pol­i­cy Analy­sis notes: ​ “ Black and His­pan­ic children’s par­ents typ­i­cal­ly have low­er incomes and low­er lev­els of edu­ca­tion­al attain­ment than white children’s par­ents. Because high­er-income and more-edu­cat­ed fam­i­lies typ­i­cal­ly can pro­vide more edu­ca­tion­al oppor­tu­ni­ties for their chil­dren, fam­i­ly socioe­co­nom­ic resources are strong­ly relat­ed to edu­ca­tion­al outcomes.”

Chil­dren from low-socioe­co­nom­ic sta­tus house­holds and com­mu­ni­ties devel­op aca­d­e­m­ic skills more slow­ly than chil­dren from high­er socioe­co­nom­ic sta­tus groups, as report­ed by the Amer­i­can Psy­cho­log­i­cal Asso­ci­a­tion. The school sys­tems in low-socioe­co­nom­ic sta­tus com­mu­ni­ties are often under-resourced , which neg­a­tive­ly impacts the aca­d­e­m­ic progress and out­comes of the stu­dents they serve. For example:

• Chil­dren from low-socioe­co­nom­ic sta­tus fam­i­lies enter high school with aver­age lit­er­a­cy skills five years behind those of high-income students.
• Indi­vid­u­als with­in the top fam­i­ly income quar­tile are 8  times more like­ly to obtain a bachelor’s degree by age 24 as com­pared to indi­vid­u­als from the low­est fam­i­ly income quartile.

The Cen­ter for Edu­ca­tion Pol­i­cy Analy­sis notes that racial achieve­ment gaps are strong­ly cor­re­lat­ed with — but not sole­ly due to — dif­fer­ences in racial socioe­co­nom­ic status.

COVID- 19 ’s Effects on Racial Dis­par­i­ties in Education

The COVID- 19 pan­dem­ic had a pro­found impact on the deliv­ery of edu­ca­tion to stu­dents of all ages. At the start of the pan­dem­ic — from April to May 2020 — access to remote learn­ing tools, such as a com­put­er and inter­net ser­vices, were crit­i­cal. And yet, just 74 % of Black house­holds had the nec­es­sary vir­tu­al learn­ing tools ​ “ usu­al­ly or always avail­able” for chil­dren in their house­hold. This rate was sub­stan­tial high­er for both Asian house­holds ( 89 %) and white house­holds ( 86 %), accord­ing to the KIDS COUNT Data Center.

Even with the right tools at home, the nation’s abrupt shift to remote learn­ing proved chal­leng­ing . It hin­dered stu­dent and teacher engage­ment, dra­mat­i­cal­ly decreased instruc­tion­al time and hin­dered stu­dent under­stand­ing. These fac­tors fueled sig­nif­i­cant learn­ing loss for stu­dents nation­wide. They also exac­er­bat­ed exist­ing racial inequities and wors­ened exist­ing achieve­ment gaps.

Between 2019 and 2022 , Black and His­pan­ic stu­dents in 20 states across the coun­try expe­ri­enced a sharp­er decline in test scores com­pared to their white peers, accord­ing to an Edu­ca­tion Recov­ery Score­card pro­duced by researchers at Har­vard, Stan­ford and Dart­mouth. While stu­dents have since regained some of these loss­es, the researchers point out that ​ “ the White-Black gap was still slight­ly larg­er in 2023 than it was in 2019 , par­tic­u­lar­ly in math.”

How We Can Com­bat Racial Inequal­i­ty in Education

Edu­ca­tion lead­ers and sys­tems must con­tin­ue to pri­or­i­tize the work of erad­i­cat­ing racial inequal­i­ty and racial dis­crim­i­na­tion in Amer­i­can schools.

Among the evi­dence-based strate­gies to con­sid­er: Same-race ele­men­tary school teach­ers have been shown to boost aca­d­e­m­ic achieve­ment among their stu­dents. In one study, Black stu­dents were ran­dom­ly assigned to at least one Black teacher in their first four years of pri­ma­ry school. This arrange­ment increased the stu­dents’ like­li­hood of grad­u­at­ing high school by 9  per­cent­age points and increased their like­li­hood of enrolling in col­lege by 6  per­cent­age points.

The Cen­ter for Amer­i­can Progress also iden­ti­fies strate­gies for real­iz­ing a more equi­table K‑ 12 aca­d­e­m­ic land­scape . It rec­om­mends advo­cat­ing for:

• equi­table fund­ing via increas­ing fed­er­al fund­ing for edu­ca­tion and pro­mot­ing fair­er and more trans­par­ent fund­ing poli­cies at the state and local levels;
• equip­ping schools with more coun­selors, nurs­es, and social work­ers rather than increas­ing sur­veil­lance and polic­ing; and
• updat­ing school bound­aries and selec­tion cri­te­ria to pro­mote racial equity.

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