article about education in america

How much does the government spend on education? What percentage of people are college educated? How are kids doing in reading and math?

Table of Contents

What is the current state of education in the us.

How much does the US spend per student?

Public school spending per student

Average teacher salary.

How educated are Americans?

People with a bachelor's degree

Educational attainment by race and ethnicity.

How are kids doing in reading and math?

Proficiency in math and reading

What is the role of the government in education?

Spending on the education system

Agencies and elected officials.

The education system in America is made up of different public and private programs that cover preschool, all the way up to colleges and universities. These programs cater to many students in both urban and rural areas. Get data on how students are faring by grade and subject, college graduation rates, and what federal, state, and local governments spending per student. The information comes from various government agencies including the National Center for Education Statistics and Census Bureau.

During the 2019-2020 school year, there was $15,810 spent on K-12 public education for every student in the US.

Education spending per k-12 public school students has nearly doubled since the 1970s..

This estimate of spending on education is produced by the National Center for Education Statistics. Instruction accounts for most of the spending, though about a third includes support services including administration, maintenance, and transportation. Spending per student varies across states and school districts. During the 2019-2020 school year, New York spends the most per student ($29,597) and Idaho spends the least ($9,690).

During the 2021-2022 school year , the average public school teacher salary in the US was $66,397 .

Instruction is the largest category of public school spending, according to data from the National Center for Educational Statistics. Adjusting for inflation, average teacher pay is down since 2010.

In 2021 , 35% of people 25 and over had at least a bachelor’s degree.

Over the last decade women have become more educated than men..

Educational attainment is defined as the highest level of formal education a person has completed. The concept can be applied to a person, a demographic group, or a geographic area. Data on educational attainment is produced by the Census Bureau in multiple surveys, which may produce different data. Data from the American Community Survey is shown here to allow for geographic comparisons.

In 2021 , 61% of the Asian 25+ population had completed at least four years of college.

Educational attainment data from the Census Bureau's Current Population Survey allows for demographic comparisons across the US.

In 2022, proficiency in math for eighth graders was 26.5% .

Proficiency in reading in 8th grade was 30.8% ., based on a nationwide assessment, reading and math scores declined during the pandemic..

The National Assessment of Educational Progress (NAEP) is the only nationally representative data that measures student achievement. NAEP is Congressionally mandated. Tests are given in a sample of schools based on student demographics in a given school district, state, or the US overall. Testing covers a variety of subjects, most frequently math, reading, science, and writing.

In fiscal year 2020, governments spent a combined total of $1.3 trillion on education.

That comes out to $4,010 per person..

USAFacts categorizes government budget data to allocate spending appropriately and to arrive at the estimate presented here. Most government spending on education occurs at the state and local levels rather than the federal.

Government revenue and expenditures are based on data from the Office of Management and Budget, the Census Bureau, and the Bureau of Economic Analysis. Each is published annually, although due to collection times, state and local government data are not as current as federal data. Thus, when combining federal, state, and local revenues and expenditures, the most recent year for a combined number may be delayed.

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Articles on US education

Displaying 1 - 20 of 114 articles.

What families need to know about how to safely store firearms at home

Kerri Raissian , University of Connecticut and Jennifer Necci Dineen , University of Connecticut

A Texas court ruling on a Black student wearing hair in long locs reflects history of racism in schools

Kenjus T. Watson , American University

Why federal efforts to protect schools from cybersecurity threats fall short

Nir Kshetri , University of North Carolina – Greensboro

‘Time warp’ takes students to Native American past to search for solutions for the future

Eric M. Anderman , The Ohio State University and Tzu-Jung Lin , The Ohio State University

1 in 4 Colorado 11th-graders skipped their state’s standardized test − geography and income help explain why

Lydia Ross , Arizona State University ; Carlos Casanova , Arizona State University ; Kathryn Chapman , University of Florida , and Sherman Dorn , Arizona State University

Higher education can be elusive for asylum-seekers and immigrants

Kerri Evans , University of Maryland, Baltimore County ; Ishara Casellas Connors , Texas A&M University , and Lisa Unangst , SUNY Empire State College

Do ‘sputnik moments’ spur educational reform? A rhetoric scholar weighs in

Mark Hlavacik , University of North Texas

Lessons for today from the overlooked stories of Black teachers during the segregated civil rights era

Marlee Bunch , University of Illinois at Urbana-Champaign

In fractious debate, GOP candidates find common ground on cause of inflation woes and need for school choice

Ryan Herzog , Gonzaga University and Celeste K. Carruthers , University of Tennessee

Year-round school: Difference-maker or waste of time?

Daniel H. Robinson , University of Texas at Arlington and Nicole Miller , Mississippi State University

Florida’s academic standards distort the contributions that enslaved Africans made to American society

Rodney Coates , Miami University

Do smartphones belong in classrooms? Four scholars weigh in

Louis-Philippe Beland , Carleton University ; Arnold Lewis Glass , Rutgers University ; Daniel G. Krutka , University of North Texas , and Sarah Rose , Staffordshire University

Short meetings could encourage teachers to stay on the job

James Martinez , University of Tennessee

Superintendent turnover is increasing and gender gaps are barely budging, but accurately assessing the consequences remains a challenge

Rachel S. White , University of Tennessee

Parents tend to choose their children’s schools based on their own educational experience

Anna Rhodes , Rice University and Julia Szabo , Rice University

40 years ago ‘A Nation at Risk’ warned of a ‘rising tide of mediocrity’ in US schools – has anything changed?

Morgan Polikoff , University of Southern California

This course uses ‘Abbott Elementary’ to examine critical issues in urban education

Sara Jones , Illinois State University

New Advanced Placement African American Studies course is a watered down version of itself

Suneal Kolluri , University of California, Riverside

Sex, drugs and alcohol are the top reasons that Texas teachers get in trouble, but overall, such cases are rare

Sarah Guthery , Texas A&M University-Commerce and Meredith Richards , Southern Methodist University

First grader who shot teacher in Virginia is among the youngest school shooters in US history

David Riedman , University of Central Florida

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Professor of Education, Furman University

Associate Professor of Educational Leadership and Policy, University of Florida

Professor of Law, University of South Carolina

Professor of Counseling Psychology, Howard University

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Research Economist - Productivity and Innovation, London School of Economics and Political Science

Associate Professor of Educational Policy, Michigan State University

Lecturer in Economics and Social Statistics, UCL

PhD student studying educational foundations, policy and practice, University of Colorado Boulder

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Professor, Leadership and School Transformation, School of Education, University of Newcastle, University of Newcastle

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The Hechinger Report

Covering Innovation & Inequality in Education

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

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• Proof Points

Americans like to believe that education can be a great equalizer, allowing even the poorest child who studies hard to enter the middle class. But when I looked at what academic researchers and federal data reports have said about the great educational divide between the rich and poor in our country, that belief turns out to be a myth. Basic education, from kindergarten through high school, only expands the disparities.

In 2015, during the Obama administration, the federal education department issued a report that showed how the funding gap between rich and poor schools grew 44 percent over a decade between 2001-2 and 2011-12. That meant that the richest 25 percent of school districts spent $1,500 more per student, on average, than the poorest 25 percent of school districts. 

I wish I could have continued to track this data between rich and poor schools to see if school spending had grown more fair. But the Trump administration crunched the numbers differently. When it issued a report in 2018 , covering the 2014-15 school year, it found that the wealthiest 25 percent of districts spent $450 more per student than the poorest 25 percent. 

That didn’t mean there was a giant 70 percent improvement from $1,500. The Trump administration added together all sources of funds, including federal funding, which amounts to 8 percent of total school spending, while the Obama administration excluded federal funds, counting only state and local dollars, which make up more than 90 percent of education funds. The Obama administration argued at the time that federal funds for poor students were intended to supplement local funds because it takes more resources to overcome childhood poverty, not to create a level playing field. 

Rather than marking an improvement, there were signs in the Trump administration data that the funding gap between rich and poor had worsened during the Great Recession if you had compared the figures apples to apples, either including or excluding federal funds. In a follow-up report issued in 2019, the Trump administration documented that the funding gap between rich and poor schools had increased slightly to $473 per student between the 2014-15 and 2015-16 school years. 

It’s not just a divide between rich and poor but also between the ultra rich and everyone else. In 2020, a Pennsylvania State University researcher documented how the wealthiest school districts in America — the top 1 percent — fund their schools at much higher levels than everyone else and are increasing their school spending at a faster rate. The school funding gap between a top 1 percent district (mostly white suburbs) and an average-spending school district at the 50th percentile widened by 32 percent between 2000 and 2015, the study calculated. Nassau County, just outside New York City on Long Island, has the highest concentration of students who attend the best funded public schools among all counties in the country. Almost 17 percent of all the top 1 percent students in the nation live in this one county. 

Funding inequities are happening in a context of increased poverty in our schools. In 2013, I documented how the number of high poverty schools had increased by about 60 percent to one out of every five schools in 2011 from one out of every eight schools in 2000. To win this unwelcome designation, 75 percent or more of an elementary, middle or high school’s students lived in families poor enough to qualify for free or reduced-price lunch. It’s since gotten worse. In the most recent federal report , covering the 2016-17 school year, one out of every four schools in America was classified as  high poverty. 

It’s not just that poverty is becoming more concentrated in certain schools; more students in the school system are poor. In 2014, I documented a 40 percent jump in the number of school-aged children living in poverty between 2000 and 2012 from one out of every seven children to one out of every five students. In the most recent report, for the 2016-17 school year, the poverty rate declined from 21 percent in 2010 to 18 percent in 2017. About 13 million children under the age of 18 were in families living in poverty.

When you break the data down by race, there are other striking patterns. One third of all Black children under 18 were living in poverty in 2016-17, compared with a quarter of Hispanic children. White and Asian children have a similar poverty rate of 11 percent and 10 percent, respectively.

Sociologists like Sean Reardon at Stanford University and Ann Owens at the University of Southern California have built a body of evidence that school segregation by income is what’s really getting worse in America, not school segregation by race. But it’s a complicated argument because Black and Latino students are more likely to be poor and less likely to be rich.  So the two things — race and poverty — are intertwined. 

In 2019, Reardon studied achievement gaps in every school in America and found that the difference in poverty rates between predominantly Black and predominantly white schools explains the achievement gaps we see and why white schools tend to show higher test scores than Black schools. When white and Black schools have the same poverty rates, Reardon didn’t see a difference in academic achievement. The problem is that Black students are more often poor and attending schools with more poor students. And other than a handful of high-performing charter schools in a few cities, he couldn’t find examples of academic excellence among schools with a high-poverty student body.

“It doesn’t seem that we have any knowledge about how to create high-quality schools at scale under conditions of concentrated poverty,” said Reardon. “And if we can’t do that, then we have to do something about segregation. Otherwise we’re consigning Black and Hispanic and low-income students to schools that we don’t know how to make as good as other schools. The implication is that you have got to address segregation.”

Previous Proof Points columns cited in this column:

The number of high-poverty schools increases by about 60 percent

Poverty among school-age children increases by 40 percent since 2000

The gap between rich and poor schools grew 44 percent over a decade

Data show segregation by income (not race) is what’s getting worse in schools

In 6 states, school districts with the neediest students get less money than the wealthiest

An analysis of achievement gaps in every school in America shows that poverty is the biggest hurdle

Rich schools get richer: School spending analysis finds widening gap between top 1% and the rest of us

This story about education inequality in America written by Jill Barshay and produced by  The Hechinger Report , a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for the  Hechinger newsletter .

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

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

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

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

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

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

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• Proc Natl Acad Sci U S A
• 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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Biden Administration Releases Revised Title IX Rules

The new regulations extended legal protections to L.G.B.T.Q. students and rolled back several policies set under the Trump administration.

By Zach Montague and Erica L. Green

Reporting from Washington

The Biden administration issued new rules on Friday cementing protections for L.G.B.T.Q. students under federal law and reversing a number of Trump-era policies that dictated how schools should respond to cases of alleged sexual misconduct in K-12 schools and college campuses.

The new rules, which take effect on Aug. 1, effectively broadened the scope of Title IX, the 1972 law prohibiting sex discrimination in educational programs that receive federal funding. They extend the law’s reach to prohibit discrimination and harassment based on sexual orientation and gender identity, and widen the range of sexual harassment complaints that schools will be responsible for investigating.

“These regulations make it crystal clear that everyone can access schools that are safe, welcoming and that respect their rights,” Miguel A. Cardona, the education secretary, said in a call with reporters.

The rules deliver on a key campaign promise for Mr. Biden, who declared he would put a “quick end” to the Trump-era Title IX rules and faced mounting pressure from Democrats and civil rights leaders to do so.

The release of the updated rules, after two delays, came as Mr. Biden is in the thick of his re-election bid and is trying to galvanize key electoral constituencies.

Through the new regulations, the administration moved to include students in its interpretation of Bostock v. Clayton County, the landmark 2020 Supreme Court case in which the court ruled that the Civil Rights Act of 1964 protects gay and transgender workers from workplace discrimination. The Trump administration held that transgender students were not protected under federal laws, including after the Bostock ruling .

In a statement, Betsy DeVos, who served as Mr. Trump’s education secretary, criticized what she called a “radical rewrite” of the law, asserting that it was an “endeavor born entirely of progressive politics, not sound policy.”

Ms. DeVos said the inclusion of transgender students in the law gutted decades of protections and opportunities for women. She added that the Biden administration also “seeks to U-turn to the bad old days where sexual misconduct was sent to campus kangaroo courts, not resolved in a way that actually sought justice.”

While the regulations released on Friday contained considerably stronger protections for L.G.B.T.Q. students, the administration steered clear of the lightning-rod issue of whether transgender students should be able to play on school sports teams corresponding to their gender identity.

The administration stressed that while, writ large, exclusion based on gender identity violated Title IX, the new regulations did not extend to single-sex living facilities or sports teams. The Education Department is pursuing a second rule dealing with sex-related eligibility for male and female sports teams. The rule-making process has drawn more than 150,000 comments.

Under the revisions announced on Friday, instances where transgender students are subjected to a “hostile environment” through bullying or harassment, or face unequal treatment and exclusion in programs or facilities based on their gender identity, could trigger an investigation by the department’s Office for Civil Rights.

Instances where students are repeatedly referred to by a name or pronoun other than one they have chosen could also be considered harassment on a case-by-case basis.

“This is a bold and important statement that transgender and nonbinary students belong, in their schools and in their communities,” said Olivia Hunt, the policy director for the National Center for Transgender Equality.

The regulations appeared certain to draw to legal challenges from conservative groups.

May Mailman, the director of the Independent Women’s Law Center, said in a statement that the group planned to sue the administration. She said it was clear that the statute barring discrimination on the basis of “sex” means “binary and biological.”

“The unlawful omnibus regulation reimagines Title IX to permit the invasion of women’s spaces and the reduction of women’s rights in the name of elevating protections for ‘gender identity,’ which is contrary to the text and purpose of Title IX,” she said.

The existing rules, which took effect under Mr. Trump in 2020, were the first time that sexual assault provisions were codified under Title IX. They bolstered due process rights of accused students, relieved schools of some legal liabilities and laid out rigid parameters for how schools should conduct impartial investigations.

They were a sharp departure from the Obama administration’s interpretation of the law, which came in the form of unenforceable guidance documents directing schools to ramp up investigations into sexual assault complaints under the threat of losing federal funding. Scores of students who had been accused of sexual assault went on to win court cases against their colleges for violating their due process rights under the guidelines.

The Biden administration’s rules struck a balance between the Obama and Trump administration’s goals. Taken together, the regulation largely provides more flexibility for how schools conduct investigations, which advocates and schools have long lobbied for.

Catherine E. Lhamon, the head of the department’s Office for Civil Rights who also held the job under President Barack Obama, called the new rules the “most comprehensive coverage under Title IX since the regulations were first promulgated in 1975.”

They replaced a narrower definition of sex-based harassment adopted under the Trump administration with one that would include a wider range of conduct. And they reversed a requirement that schools investigate only incidents alleged to have occurred on their campuses or in their programs.

Still, some key provisions in the Trump-era rules were preserved, including one allowing informal resolutions and another prohibiting penalties against students until after an investigation.

Among the most anticipated changes was the undoing of a provision that required in-person, or so-called live hearings, in which students accused of sexual misconduct, or their lawyers, could confront and question accusers in a courtroom-like setting.

The new rules allow in-person hearings, but do not mandate them. They also require a process through which a decision maker could assess a party or witness’s credibility, including posing questions from the opposing party.

“The new regulations put an end to unfair and traumatic grievance procedures that favor harassers,” Kel O’Hara, a senior attorney at Equal Rights Advocates. “No longer will student survivors be subjected to processes that prioritize the interests of their perpetrators over their own well being and safety.”

The new rules also allow room for schools to use a “preponderance of evidence” standard, a lower burden of proof than the DeVos-era rules encouraged, through which administrators need only to determine whether it was more likely than not that sexual misconduct had occurred.

The renewed push for that standard drew criticism from legal groups who said the rule stripped away hard-won protections against flawed findings.

“When you are dealing with accusations of really one of the most heinous crimes that a person can commit — sexual assault — it’s not enough to say, ‘50 percent and a feather,’ before you brand someone guilty of this repulsive crime,” said Will Creeley, the legal director of the Foundation for Individual Rights and Expression.

The changes concluded a three-year process in which the department received 240,000 public comments. The rules also strengthen protections for pregnant students, requiring accommodations such as a bigger desk or ensuring access to elevators and prohibiting exclusion from activities based on additional needs.

Title IX was designed to end discrimination based on sex in educational programs or activities at all institutions receiving federal financial assistance, beginning with sports programs and other spaces previously dominated by male students.

The effects of the original law have been pronounced. Far beyond the impact on school programs like sports teams, many educators credit Title IX with setting the stage for academic parity today. Female college students routinely outnumber male students on campus and have become more likely than men of the same age to graduate with a four-year degree.

But since its inception, Title IX has also become a powerful vehicle through which past administrations have sought to steer schools to respond to the dynamic and diverse nature of schools and universities.

While civil rights groups were disappointed that some ambiguity remains for the L.G.B.T.Q. students and their families, the new rules were widely praised for taking a stand at a time when education debates are reminiscent to the backlash after the Supreme Court ordered schools to integrate.

More than 20 states have passed laws that broadly prohibit anyone assigned male at birth from playing on girls’ and women’s sports teams or participating in scholastic athletic programs, while 10 states have laws barring transgender people from using bathrooms based on their gender identity.

“Some adults are showing up and saying, ‘I’m going to make school harder for children,” said Liz King, senior program director of the education equity program at the Leadership Conference on Civil and Human Rights. “It’s an incredibly important rule, at an incredibly important moment.”

Schools will have to cram over the summer to implement the rules, which will require a retraining staff and overhauling procedures they implemented only four years ago.

Ted Mitchell, the president of the American Council on Education, which represents more than 1,700 colleges and universities, said in a statement that while the group welcomed the changes in the new rule, the timeline “disregards the difficulties inherent in making these changes on our nation’s campuses in such a short period of time.”

“After years of constant churn in Title IX guidance and regulations,” Mr. Mitchell said, “we hope for the sake of students and institutions that there will be more stability and consistency in the requirements going forward.”

Zach Montague is based in Washington. He covers breaking news and developments around the district. More about Zach Montague

Erica L. Green is a White House correspondent, covering President Biden and his administration. More about Erica L. Green