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Structural Racism in Schools: A View through the Lens of the National School Social Work Practice Model

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Jandel Crutchfield, Kate L Phillippo, Andy Frey, Structural Racism in Schools: A View through the Lens of the National School Social Work Practice Model, Children & Schools , Volume 42, Issue 3, July 2020, Pages 187–193, https://doi.org/10.1093/cs/cdaa015

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Structural racism—implicitly discriminatory practices and policies that have negative consequences for individuals and groups of color—is a powerful force in contemporary American society, including in our public education system. This article explores the potential for school social workers (SSWers) to address structural racism through the use of the national school social work (SSW) practice model as a tool to guide systemic, ecologically oriented intervention within schools and educational policy spaces. In this article, the authors review data on racial disparities in educational attainment, placement, opportunity, and discipline practices that have led to increased attention to structural racism in schools. They then discuss and describe the national SSW practice model and its suitability for the structural interventions in response to structural racism in schools. Finally, they provide recommendations for how SSWers can respond effectively to this pressing social problem. These recommendations include a list of resources for addressing structural racism.

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The State of Critical Race Theory in Education

• Posted February 23, 2022
• By Jill Anderson
• Diversity, Equity, and Inclusion
• Moral, Civic, and Ethical Education

When Gloria Ladson-Billings set out in the 1990s to adapt critical race theory from law to education, she couldn’t have predicted that it would become the focus of heated school debates today.

Over the past couple years, the scrutiny of critical race theory — a theory she pioneered to help explain racial inequities in education — has become heavily politicized in school communities and by legislators. Along the way, it has also been grossly misunderstood and used as a lump term about many things that are not actually critical race theory, Ladson-Billings says. 

“It's like if I hate it, it must be critical race theory,” Ladson-Billings says. “You know, that could be anything from any discussions about diversity or equity. And now it's spread into LGBTQA things. Talk about gender, then that's critical race theory. Social-emotional learning has now gotten lumped into it. And so it is fascinating to me how the term has been literally sucked of all of its meaning and has now become 'anything I don't like.'”

In this week’s Harvard EdCast, Ladson-Billings discusses how she pioneered critical race theory, the current politicization and tension around teaching about race in the classroom, and offers a path forward for educators eager to engage in work that deals with the truth about America’s history. 

TRANSCRIPT:

Jill Anderson:   I'm Jill Anderson. This is the Harvard EdCast.

Gloria Ladson-Billings never imagined a day when the words critical race theory would make the daily news, be argued over at school board meetings, or targeted by legislators. She pioneered an adaptation of critical race theory from law to education back in the 1990s. She's an educational researcher focused on theory and pedagogy who at the time was looking for a better way to explain racial disparities in education.

Today the theory is widely misunderstood and being used as an umbrella term for anything tied to race and education. I wondered what Gloria sees as a path forward from here. First, I wanted to know what she was thinking in this moment of increased tension and politicization around critical race theory and education.

Well, if I go back and look at the strategy that's been employed to attack critical race theory, it actually is pretty brilliant from a strategic point of view. The first time that I think that general public really hears this is in September of '20 when then president and candidate Donald Trump, who incidentally is behind in the polls, says that we're not going to have it because it's going to destroy democracy. It's going to tear the country apart. I'm not going to fund any training that even mentions critical race theory.

And what's interesting, he says, "And anti-racism." Now he's now paired two things together that were not really paired together in the literature and in practice. But if you dig a little deeper, you will find on the Twitter feed of Christopher Rufo, who is from the Manhattan Institute, two really I think powerful tweets. One in which he says, "We're going to render this brand toxic." Essentially what we're going to do is make you think, whenever you hear anything negative, you will think critical race theory. And it will destroy all of the, quote, cultural insanities. I think that's his term that Americans despise. There's a lot to be unpacked there, which Americans? Who is he talking about? What are these cultural insanities? And then there's another tweet in which he says, "We have effectively frozen the brand." So anytime you think of anything crazy, you think critical race theory. So he's done this very effective job of rendering the term, in some ways without meaning. It's like if I hate it, it must be critical race theory.

You know, that could be anything from any discussions about diversity or equity. And now it's spread into LGBTQA things. Talk about gender, then that's critical race theory. Social emotional learning has now got lumped into it. And so it is fascinating to me how the term has been literally sucked of all of its meaning and has now become anything I don't like.

Jill Anderson:  Can you break it down? What is critical race theory? What isn't it?

Gloria Ladson-Billings: Let me be pretty elemental here. Critical race theory is a theoretical tool that began in legal studies, in law schools, in an attempt to explain racial inequity. It serves the same function in education. How do you explain the inequity of achievement, the racial inequity of achievement in our schools?

Now let's be clear. The nation has always had an explanation for inequity. Since 1619, it's always had a explanation. And indeed from 1619 to the mid 20th century, that explanation was biogenetic. Those people are just not smart enough. Those people are just not worthy enough. Those people are not moral enough.

In fact across the country, we had on college and university campuses, programs and departments in eugenics. If you went to the World's Fair or the World Expositions back in the turn of the 20th century, you could see exhibits with, quote, groups of people from the best group who was always white and typically blonde and blue eyed, to the worst group, which is typically a group of Africans, generally pygmies. So the idea is you can rank people. So we've always had an explanation for why we thought inequity exists.

Somewhere around the mid 20th century, 1950s, you'll get a switch that says, well, no, it's really not genetic it's that some groups haven't had an equal opportunity. That was a powerful explanation. So one of the things that you begin to see around mid 1950s is legislation and court decisions, Brown versus Board of Education. You start to see the Voters Rights Act. You see the Civil Rights Act. You see affirmative action going into the 1960s. And yeah, I think that's a pretty good, powerful explanatory model.

Except they all get rolled back. 1954, Brown v. Board of Education . How many of our kids are still in segregated schools in 2022? So that didn't hold. Affirmative action. The court's about to hear that, right? Because of actually the case that's coming out of Harvard. Voters rights. How many of our states have rolled back voters rights? You can't give a person a bottle of water who was waiting in line in Georgia. We're shrinking the window for when people can vote.

So all of the things that were a part of the equality of opportunity explanation have rolled away. Critical race theory's explanation for racial inequality is that it is baked into the way we have organized the society. It is not aberrant. It's not one of those things that we all clutch our pearls and say, "Oh my God, I can't believe that happened." It happens on a regular basis all the time. And so that's really one of the tenets that people are uncomfortable hearing. That it's not abnormal behavior in our society for people to react in racist ways.

Jill Anderson: My understanding is that critical race theory is not something that is taught in schools. This is an older, like graduate school level, understanding and learning in education, not something for K–12 kids, not something my kid's going to learn in elementary school.

Gloria Ladson-Billings: You're exactly right. It is not. First of all, kids in K12 don't need theory. They need some very practical hands-on experiences. So no, it's not taught in K12 schools. I never even taught it as a professor at the University of Wisconsin. I didn't even teach it to my undergraduates. They had no use for it. My undergraduates were going to be teachers. So what would they do with it? I only taught it in graduate courses. And I have students who will tell you, "I talked with Professor Ladson-billings about using critical race theory for my research," and she looked at what I was doing and said, "It doesn't apply. Don't use it."

So I haven't been this sort of proselytizer. I've said to students, if what you're looking at needs an explanation for the inequality, you have a lot of theories that you can choose from. You can choose from feminist theory. That often looks at inequality across gender. You could look at Marx's theory. That looks at inequality across class. There are lots of theories to explain inequality. Critical race theory is trying to explain it across race and its intersections.

Jill Anderson:  We're seeing this lump definition falling under critical race theory, where it could be anything. It could be anti-racism, diversity and equity, multicultural education, anti-racism, cultural [inaudible 00:09:15]. All of it's being lumped together. It's not all the same thing.

Gloria Ladson-Billings: Well, and in some ways it's proving the point of the critical race theorists, right? That it's kind normal. It's going to keep coming up because that's the way you see the world. I mean, here's an interesting lumping together that I think people have just bought whole cloth. That somehow Nikole Hannah-Jones' 1619 is critical race theory. No, it's not.

No. It. Is. Not. It is a journalist's attempt to pull together strands of a date that we tend to gloss over and say, here are all the things were happening and how the things that happened at this time influenced who we became. It's really interesting that people have jumped on that. And there is another book that came out, and it also came out of a newspaper special from the Hartford Courant years ago called Complicity. That book is set in New England and it talks about how the North essentially kept slavery going.

And when it was published by the Hartford Courant, Connecticut, and particularly Hartford said, we want a copy of this in every one of our middle and high schools to look out at what our role has been. Because the way we typically tell you our history is to say, the noble and good North and then the backward and racist South. Well, no, the entire country was engaged in the slave trade. And it benefited folks across the nation.

That particular special issue, which got turned into a book hasn't raised an eyebrow. But here comes Nikole Hannah-Jones. And initially, of course, she won a Pulitzer for it and people were celebrating her. But it's gotten lumped into this discussion that essentially says you cannot have a conversation about race.

What I find the most egregious about this situation is we are taking books out of classrooms, which is very anti-democratic. It is not, quote, the American way. And so you're saying that kids can't read the story of Ruby Bridges. It's okay for Ruby Bridges at six years old to have to have been escorted by federal marshals and have racial epithets spewed at her. It's just not okay for a six year old today to know that happened to her. I mean, one of the rationales for not talking about race, I don't even say critical race theory, but not talking about race in the classroom is we don't want white children to feel bad.

My response is, well great, but what were you guys in the 1950s and sixties when I was in school. Because I had to sit there in a mostly white classroom in Philadelphia and read Huckleberry Finn , with Mark Twain with a very liberal use of the n-word. And most of my classmates just snickering. I'd take it. I'd read it. It didn't make me feel good. I had to read Robinson Crusoe . I had to read Margaret Mitchell's Gone With The Wind . I had to read Heart Of Darkness .

All of these books which we have canonized, are books of their time. And they often make us feel a particular kind way about who we are in this society. But all of a sudden one group is protected. We can't let white children feel bad about what they read.

Jill Anderson: I was reading your most recent book, Critical Race Theory in Education, a Scholars Journey , and I was struck by when you started to do this work and this research, and adapt it from law back in the early 1990s. You talked about presenting this for the first time, or one of the first times. And there was obviously a group excited by it, a group annoyed by it. I look at what's happening now and I see parents and educators. Some are excited by a movement to teach children more openly and honestly about race. And then there's going to be those who are annoyed by it. You've been navigating these two sides your whole life, your whole career. So what do you tell educators who are eager, and open, and want to do this work, but they're afraid of the opposition?

Gloria Ladson-Billings:  Well, I think there's a difference between essentially forcing one's ideas and agenda on students, and having kids develop the criticality that they will need to participate in democracy. And whenever we have pitched battles, we've been talking about race, but we've had the same kind of conversation around the environment, right? That you cannot be in coal country telling people that coal is bad, because people are making their living off of that coal. So we've been down this road before.

What I suggest to teachers is, number one, they have to have good relationships with the parents and community that they are serving, and they need to be transparent. I've taught US History for eighth graders and 11th graders before going into academe, and we've had to deal with hard questions. But there's a degree to which the community has always trusted that I had their students' best interests at heart, that I want them to be successful, that I want them to be able to make good decisions as citizens.

That's the bigger mission, I think, of education. That we are not just preparing people to go into the workplace. We are preparing people to go into voting booths, and to participate in healthy debate. The problem I'm having with critical race theory is I'm having a debate with people who don't know what we're debating. You know, I told one interview, I said, "It's like debating a toddler over bedtime. That's not a good debate." You can't win that debate. The toddler doesn't understand the concept. It's just that I don't want to do it.

I will say following the news coverage that I don't believe that all of these people out there are parents. I believe that there is a large number of operatives whose job it is to gin up sentiment against any forward movement and progress around racial equality, and equity, and diversity.

You know, to me, what should be incensing people was what they saw in Charlottesville, with those people, with those Tiki torches. What should be incensing people is what they saw January 6th. People lost their lives in both of those incidents. Nobody's lost their lives in a critical race theory discussion. You know?

I'm someone who believes that debate is healthy. And in fact debate is the only thing that you can have in a true democracy. The minute you start shutting off debate, the minute you say that's not even discussable, then you're moving towards totalitarianism. You know? That's what happened in the former Soviet Union and probably now in Russia. That's what has happened in regimes that say, no other idea is permitted, is discussable. And that's not a road that I think we should be walking here.

Jill Anderson: I feel like we're getting lost in the terminology, which we've talked about. And for school leaders, I wonder if the conversation needs to start with local districts in their communities debunking, or demystifying, or telling the truth about what critical race theory is, that kids aren't learning it in the schools. That that's not what it's about. Does it not even matter at this point because people are always going to be resistant to the things that you just even mentioned?

Gloria Ladson-Billings:  I'm a bit of a sports junkie, so I'll use a sports metaphor here. I'm just someone who would rather play offense than defense. I think if you get into this debate, you are on the defensive from the start. For me, I want to be on the offense. I want to say, as a school district, here are our core values. Here's what we stand for. Many, many years ago when I began my academic career, I started it at Santa Clara University, which is a private Catholic Jesuit university. And students would sometimes bristle at the discussions we would have about race and ethnicity, and diversity and equality.

And I'd always pull out the university's mission statement. And I'd say, "You see these words right here around social justice? That's where I am with this work. I don't know what they're doing at the business school on social justice, but I can tell you that the university has essentially made a commitment it to this particular issue. Now we can debate whether or not you agree with me, but I haven't pulled this out of thin air."

So if I'm a school superintendent, I want to say, "Here are core values that we have." I'm reminded of many years ago. I was supervising a student teacher. It was a second grade. And she had a little boy in a classroom and they were doing something for Martin Luther King. It might have been just coloring in a picture of him with some iconic statement. And this one little boy put a big X on it. And she said, "Why did you do that?" And his response was, "We don't believe in Martin Luther King in my house." So she said, "Wow, okay, well, why not?" And he really couldn't articulate. She says, "Well, tell me, who's your friend in this classroom?" And one of the first names out of his mouth was a little Black boy.

And she said, "Do you know that he's a lot like Martin Luther King? You know, he's a little boy. He's Black." She was worried about where this was headed and didn't know what to do as a student teacher, because she's not officially licensed to teach at this point. And I shared with her our strategy. I said, "Why don't you talk with your cooperating teacher about what happens and see what she says. If she doesn't seem to want to do anything, casually mention, don't go marching to the principal's office. But when you have a chance to interact with the principal, you might say something I had the strangest encounter the other day and then share it." Well, she did that.

The principal called the parents in and said, "Your child is not in trouble, but here's what you need to know about who we are and what we stand for."

Jill Anderson:  Wow.

Gloria Ladson-Billings:  You know? And so again, it wasn't like let's have a big school board meeting. Let's string up somebody for saying something. It wasn't tearing this child down. But it was reiterating, here are our core values. I think schools can stand on this. They can say, "This is what we stand for. This is who we are." They don't ever have to mention the word critical race theory.

The retrenchment we are seeing in some states, I think it was a textbook that they were going to use in Texas that essentially described enslaved people as workers. That's just wrong. That's absolutely wrong. And I can tell you that if we don't teach our children the truth, what happens when they show up in classes at the college level and they are exposed to the truth, they are incensed. They are angry and they cannot understand, why are we telling these lies?

We don't have to make up lies about the American story. It is a story of both triumph and defeat. It is a story of both valor and, some cases, shame. Slavery actually happened. We trafficked with human beings, and there's a consequence to that. But it doesn't mean we didn't get past it. It doesn't mean we didn't fight a war over it, and decide that's not who we want to be.

Jill Anderson:  What's the path forward? What can we do to make sure that students are supported and learning about their own history so that they are prepared to go out into a diverse global society?

Gloria Ladson-Billings:  I'm perhaps an unrepentant optimist, because I think that these young people are not fooled by this. You know, when they started, quote, passing bans and saying, "We can't have this and we won't have this," I said, "Nobody who's doing this understands anything about child and adolescent development." Because how do you get kids to do something? You tell them they can't do.

So I have had more outreach from young people asking me, tell me about this. What is this? These young people are burning up Google looking for what is this they're trying to keep from us? So I have a lot of faith in our youth that they are not going to allow us to censor that. Everything you tell them, they can't read, those are the books they go look for. You know, I have not seen a spate in reading like this in a very long time.

So I think it's interesting that people don't even understand something as basic as child development and adolescent development. But I do think that the engagement of young people, which we literally saw in the midst of the pandemic and the post George Floyd, the incredible access to information that young people have will save us. You know, it's almost like people feel like this is their last bastion and they're not going to let people take whatever privilege they see themselves having away from them. It's not sustainable. Young people will not stand for it.

Jill Anderson:  Well, I love that. And it's such a great note to end on because it feels good to think that there is a path forward, because right now things are looking very scary. Thank you so much.

Gloria Ladson-Billings:  Well, you're quite welcome. And I will tell you, again sports metaphor, I'm an, again, unrepentant 76ers fan. I realize you're in Massachusetts with those Celtics. But trust me, the 76ers. Okay? One of my favorite former 76ers is Allen Iverson and he has a wonderful line, I believe when he was inducted into the Hall of Fame. He said, "My haters have made me great."

Well, I will tell you that I had conceived of that book on critical race theory well before Donald Trump made his statement in September of 2020. And I thought, "Okay, here's another book which will sell a modest number of copies to academics." The book is flying off the shelves. Y'all keep talking about it. You're just making me great.

Jill Anderson:  Maybe it will start the revolution that we need.

Gloria Ladson-Billings: Well, thank you so much.

Jill Anderson:  Thank you. Gloria Ladson-billings is a professor emerita at the University of Wisconsin at Madison. She is the author of many books, including the recent Critical Race Theory in Education, a Scholar's Journey . I'm Jill Anderson. This is the Harvard EdCast produced by the Harvard Graduate School of Education. Thanks for listening.

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Systematic Review of Theoretical Perspectives Guiding the Study of Race and Racism in Higher Education Journals

• Published: 06 January 2024
• Volume 49 , pages 247–269, ( 2024 )

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• Gloria Crisp   ORCID: orcid.org/0000-0003-1645-910X 1 ,
• Luis Alcázar 1 ,
• Jeff Ryan Sherman 1 ,
• Joseph Schaffer-Enomoto 1 &
• Natalie Rooney 1  

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Our study provides a review of theories that were used to study race and racism between 2010 and 2019 in higher education. We conducted a content analysis to identify concepts, statements and models used in higher education studies focused on race and racism in the three most highly read United States higher education journals. We also identified salient characteristics of studies focused on race and racism that applied critical race theory (CRT) and other frequently used theories and frameworks. Across the 172 reviewed studies, over 130 concepts, statements and models were identified that can be taken up by scholars and equity-minded higher education practitioners. Findings also offer direct implications and suggestions for future research focused on race and racism.

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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 .

Data Availability.

The banality of racism in education

Subscribe to how we rise, jon valant jon valant director - brown center on education policy , senior fellow - governance studies @jonvalant.

June 4, 2020

A few years ago, I ran a study with a colleague, Daniel Newark, of how Americans think about test score gaps in education. It featured a survey experiment with a nationally representative sample of adults. The study design let us test for differences in how Americans see Black-white, Hispanic-white, and wealthy-poor gaps. The study’s main finding was that Americans are far more concerned about, and willing to address, wealth-based gaps than race- and ethnicity-based gaps.

The finding that has stuck with me the most, though, came from a question about how people explain the gaps that exist today. We asked, “How much of the difference in test scores between white students and Black students can be explained by discrimination against Blacks or injustices in society?” Nearly half (44%) of respondents chose “None.” Only 10% chose “A great deal.”

That 44% figure still feels stunning, and plainly wrong—especially in the aftermath of George Floyd’s killing. We are a country only a century-and-a-half removed from the enslavement of African Americans and its accompanying anti-literacy laws, which prohibited teaching slaves to read and write. The end of that era led not to some type of egalitarian or meritocratic society—or any sincere, sustained attempt to get there—but rather to the Jim Crow laws and de jure segregation of yesterday and the de facto segregation and structural racism of today. We are not a country in which current disparities just reflect how hard different groups of people are trying. Yet respondents to our survey were far more inclined to attribute gaps to perceived deficiencies in Black parenting and student motivation than to these profound inequities.

I don’t know exactly what attitudes and experiences led to those responses. Undoubtedly, many reflect individual prejudices and failures of empathy. But I also worry that the manifestations of structural racism have become such a fixed, omnipresent part of the educational landscape that it is hard for many of us to see them.

Some of those manifestations are not explicitly in education policy or practice, but they affect students nonetheless. There are exclusionary zoning policies that keep families that can’t afford single-family homes out of high-performing school districts; tax policies that prevent Black wealth accumulation (and corresponding spending on educational resources); and mass-incarceration practices that remove parents from children’s homes and strain those left behind.

Other manifestations are direct matters of education policy and practice . Some are subtle decisions that happen largely out of sight, day after day, like missed opportunities to assign students of color to advanced coursework and excessive discipline practices that send misguided messages. Others are there for us to see: funding levels that leave many high-poverty schools inadequately resourced; attendance boundaries that erect barriers to desirable schools; and test-based accountability measures that stack the deck against high-poverty schools by emphasizing student proficiency over growth.

If there’s a silver lining for education in the simultaneous crises of COVID-19 and police brutality, maybe it’s an increased public willingness—however fleeting—to take a closer look at our education systems and the countless inequities they inherit, reproduce, and create. Of course, not everyone will want to look. Some will, though, and perhaps they will see just how defining those inequities are and what we could do about them.

Part of the responsibility for clearer vision lies with the education community, which must speak clearly and honestly about the depth and causes of educational inequity. A growing chorus of scholars—including Gloria Ladson-Billings , Prudence Carter, and Kevin Welner —argues that we have done a poor job of conceptualizing and communicating about inequities. In particular, the term “achievement gap,” while useful in increasing public recognition of a problem, connotes a failure of low-scoring students (who do not or cannot “achieve”) rather than a societal failure in creating the opportunity gaps that produce those scores. An interesting new paper by David Quinn shows that an “achievement gap” framing can, though does not always, lead people to underestimate Black students’ performance—a topic that warrants further study.

Neither the COVID-19 outbreak nor the national outcry over police brutality is, at its core, an education story. But the issues surfaced by those crises are familiar. Education rarely has a lurid moment—what political scientists call a “focusing event”—that, while horrific, can draw attention to an issue and mobilize action. When we do, like the Parkland shooting, it seldom points directly at issues of race and inequality. Rather, moments of educational inequity happen quietly, day after day, in places like classrooms and school-board meeting rooms, often at the hands of people who mean no harm.

In many ways, it’s this banality that feels so dangerous. It’s that so much of the problem lies in plain sight and still can be so difficult for many of us to see. Hopefully the circumstances of the moment will help us see those problems, and their solutions, more clearly.

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