The Economic Context of Higher Education Expansion: Race, Gender, and Household Finances Across Cohorts and Generations

Abstract

This article assesses how the economic context of higher education expansion since the mid-20th century has shaped families’ financial lives—in terms of income and wealth/debt—as well as how these trends have differed for Black and White women and men. We use data from the NLSY-79 (comprising trailing-edge Baby Boomers) and NLSY-97 (comprising early Millennials) to show how academically similar students in these two cohorts fared in terms of educational attainment, household income, household wealth, and total student debt accrued by age 35. While we discuss findings across race-gender groups, our results call attention to the education-related economic disadvantages faced by Black women that have accelerated across cohorts. Over time, Black women’s educational attainment has increased substantially, and high-achieving Black women, in particular, have become uniquely likely to progress beyond the BA. But while high-achieving Black women have made many advances in higher education, they also have become more likely than similarly high-achieving White men, White women, and Black men to have zero or negative wealth at the household level, and to accrue student debt for themselves and for their children. Our findings demonstrate that the costs of expanded access to credit for higher education have not been borne equally across race, gender, and achievement, and that these patterns have multigenerational financial consequences for college attendees and their families.

Full text

Vol:.(1234567890)
Journal of Family and Economic Issues (2024) 45:430–443
https://doi.org/10.1007/s10834-023-09918-8
1 3
ORIGINAL PAPER
The Economic Context ofHigher Education Expansion: Race, Gender,
andHousehold Finances Across Cohorts andGenerations
NatashaQuadlin1 · JordanA.Conwell2· ShivaRouhani1
Accepted: 26 May 2023 / Published online: 1 July 2023
© The Author(s) 2023
Abstract
This article assesses how the economic context of higher education expansion since the mid-20th century has shaped families’
financial lives—in terms of income and wealth/debt—as well as how these trends have differed for Black and White women
and men. We use data from the NLSY-79 (comprising trailing-edge Baby Boomers) and NLSY-97 (comprising early Millen-
nials) to show how academically similar students in these two cohorts fared in terms of educational attainment, household
income, household wealth, and total student debt accrued by age 35. While we discuss findings across race-gender groups,
our results call attention to the education-related economic disadvantages faced by Black women that have accelerated across
cohorts. Over time, Black women’s educational attainment has increased substantially, and high-achieving Black women,
in particular, have become uniquely likely to progress beyond the BA. But while high-achieving Black women have made
many advances in higher education, they also have become more likely than similarly high-achieving White men, White
women, and Black men to have zero or negative wealth at the household level, and to accrue student debt for themselves and
for their children. Our findings demonstrate that the costs of expanded access to credit for higher education have not been
borne equally across race, gender, and achievement, and that these patterns have multigenerational financial consequences
for college attendees and their families.
Keywords Race· Gender· Higher education· Income and wealth inequality· Student loan debt
Introduction
Rates of college attendance and completion have increased
markedly over the past 50years (Bowen etal., 2009; DiPrete
& Buchmann, 2013; Quadlin & Powell, 2022). Because
higher education is an important predictor of employment
status, occupational prestige, wages, and other economic
outcomes (Carnevale etal., 2013; Hout, 2011; Lemieux,
2006), college attendance theoretically should be a boon
to households’ financial standing in one generation as well
as families’ economic mobility between generations. But
at the same time that college attendance has become more
common, the cost of attending college has increased dra-
matically, alongside the expansion of credit and student debt
(Addo etal., 2016; Dwyer, 2018; Dwyer etal., 2012; Houle,
2014a; Houle & Addo, 2022; Zaloom, 2019). This ultimately
creates liabilities for individuals hoping to enhance their
human capital with a college credential—especially those
who aspire to graduate and professional degrees, as levels
of student debt often are highest among these individuals
(Pyne & Grodsky, 2020).
These trends are consequential in the context of racial
and intersectional (here, race-gender) inequality. Trends in
higher education expansion are nested within historical and
continued structural racism that advantages Whites and dis-
advantages Blacks and other populations of color (Bonilla-
Silva, 1997). Black students are more likely to complete
college than they once were, and this is especially the case
for Black women, whose rates of college completion have
exceeded Black men’s since 1920s birth cohorts (McDaniel
etal., 2011). However, Black college graduates face multi-
pronged disadvantages that threaten their ability to attain
high income and wealth. Black college graduates face racial
discrimination in the labor market, which suppresses their
wages and occupational standing (Gaddis, 2015; Nunley
* Natasha Quadlin
quadlin@soc.ucla.edu
1 Department ofSociology, University ofCalifornia, Los
Angeles, 264 Haines Hall, 375 Portola Plaza, LosAngeles,
CA90095, USA
2 University ofTexas atAustin, Austin, USA
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

431Journal of Family and Economic Issues (2024) 45:430–443
1 3
etal., 2015); they accrue disproportionately high amounts
of student debt with less favorable repayment terms than that
of their White peers (Houle & Addo, 2019, 2022; Quadlin &
Conwell, 2021); and they have much less family wealth than
comparable White students, giving them a weaker safety net
in young adulthood (Massey etal., 2003). Because Black
women’s participation in higher education is systematically
higher than Black men’s (especially among Black women
with relatively strong academic preparation, as we will dis-
cuss throughout this article), these are not simply racialized
patterns of disadvantage, but instead they are deeply inter-
sectional (Crenshaw, 1989), with Black women being more
likely than Black men to be exposed.
Although these are not new observations, little research
explicitly brings these trends in conversation with each other
to understand the broader economic context of educational
expansion for Black and White women and men and their
families. In this paper, we examine how students in two dis-
tinct cohorts with similar academic credentials fared in terms
of their educational attainment, household income, house-
hold wealth, and student debt. This approach is informed
by prior research on the unequal economic origins of high-
achieving students across race-gender groups (Quadlin &
Conwell, 2021). As college has become a normative stage
in the life course, we might expect that students’ chances of
attending college have been lifted, especially among those
with strong academic credentials. Yet, the economic conse-
quences of such exposure have been uneven. Specifically,
we make comparisons across race-gender groups among
students with similar standardized test scores, as we will
discuss throughout. Such test scores are commonly used as
shorthand for student “ability”; and while we and many oth-
ers object to this characterization because it overlooks the
strong influence of socioeconomic status and other aspects
of social (dis)advantage on such measures (Riegle-Crumb
etal., 2019), our analyses also underscore that the life course
economic payoffs to high test scores (and, by extension, their
relatively high probability of college attendance) are unequal
across race-gender groups.
The data come from two nationally representative data-
sets representing cohorts with distinct experiences in higher
education and the labor market. Respondents in the National
Longitudinal Survey of Youth, 1979 Cohort (NLSY-79)
are trailing-edge Baby Boomers, born between 1957 and
1964. Respondents in the National Longitudinal Survey of
Youth, 1997 Cohort (NLSY-97) are early Millennials, born
between 1980 and 1984. We use these datasets to show how
Black and White women and men with equivalent test scores
fared in these two cohorts in terms of their educational and
household economic attainment, thus broadly showing how
changes in historical context correspond with shifts in inter-
sectional economic inequality among those with comparable
measured college readiness.
Higher Education Expansion
andImplications forRace‑Gender Inequality
As much research has shown, college enrollment and com-
pletion in the U.S. have increased dramatically over the
past several decades. In 1970, when the NLSY-79 respond-
ents were in elementary and middle school, 11 percent of
adults ages 25 and older had attained a bachelor’s degree.
By 2017, that figure had increased to 34 percent (United
States Census Bureau, 2017)—not to mention the sizeable
number of Americans who start but do not complete a
bachelor’s degree, estimated at about 40 percent of those
who ever enroll in bachelor’s degree-granting institutions
(Snyder etal., 2019).
At the same time that enrollment in higher education
has increased, so too has the cost of attending college.
Students in 1987 could attend a public 4-year university,
including room and board, for about $9000/year in 2017
dollars. Today, that figure has increased to about $21,000/
year, although the sticker prices at some elite private insti-
tutions approach $80,000/year (College Board, 2019). The
reasons for these price increases are complex and ulti-
mately outside the scope of this article, but social scien-
tists have pointed to state disinvestment, coupled with a
reliance on individual financing and debt, as some of the
major social forces contributing to these trends (Houle
& Addo, 2022; Quadlin & Powell, 2022; Zaloom, 2019).
Prior research on how college attendance fits into the
economic life course has unevenly attended to consequen-
tial interrelationships between income, wealth, and debt.
A prominent literature in the social sciences, for exam-
ple, focuses on identifying the causal “value-added” of
college attendance, especially colleges of a given level
of prestige or selectivity (e.g., Dale & Krueger, 2002).
These studies tend to focus on individual income as the
primary outcome variable, consistent with the perspective
that college is an investment in one’s own human capital
(Becker, 1964). By comparison, the literature on student
debt understandably has been more even-handed about
both the promise and perils of investing in one’s education
through access to credit. Dwyer and colleagues (2012),
for example, describe student debt as a “double-edged
sword,” or an important facilitator of opportunity as well
as a great liability. Given that these liabilities are unevenly
borne across social groups, as we discuss further below,
our main analyses consider the patterning of educational
attainment, income, and wealth alongside the patterning
of student debt.
In the case of student debt, for the older cohort in our
study (i.e., the NLSY-79), we can incorporate intergen-
erational measures that capture student debt for oneself
as well as one’s children. Parent loans, including but not
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

432 Journal of Family and Economic Issues (2024) 45:430–443
1 3
limited to PLUS loans, are often overlooked in research,
but are a potentially meaningful source of intergenera-
tional burden (Cepa, 2021). Such loans require parents
to take on economic liabilities at a potentially precarious
time; while many of these parents are prime-aged work-
ers, and thus are in a relatively good position to repay
debt, they also may be near retirement and thus not ideally
equipped to take on a large and/or long-term economic
investment (Fletcher etal., 2020). To the extent that parent
loans are being disbursed unequally across race-gender
groups, this would indicate inequality in terms of who is
being further stretched at a potentially vulnerable point in
the life course.
We focus on how these measures have unfolded for Black
and White women and men. As background, much recent
research has discussed two major trends with respect to race
and gender in higher education. The first is racial dispari-
ties in student debt (see Houle & Addo, 2022). Although
Black students are more likely to enroll in and complete col-
lege than they once were, the average Black college student
hails from a far less economically advantaged family than
that of their White peers. Black students are more likely
to accrue debt than White students (Houle, 2014b); among
those with debt, Black students owe $5000–10,000 more
than White students on average (Jackson & Reynolds, 2013).
Black students also are more likely than White students to
default on their loans (Gross etal., 2009; Scott-Clayton &
Li, 2016) and experience financial distress because of their
loans (Martin & Dwyer, 2021). In fact, scholars recently
have argued that Black students have been incorporated in
higher education under terms of “predatory inclusion,” such
that they have access to postsecondary institutions, but in
a way that leaves them financially vulnerable (Seamster &
Charron-Chénier, 2017).
The second trend of interest is the “rise of women,” that
is, women’s steady gains in higher education that led to a
reversal of the gender gap in college enrollment and comple-
tion in the mid-1980s (Buchmann & DiPrete, 2006; DiPrete
& Buchmann, 2013; Goldin etal., 2006). While much
research discusses this trend without regard to race, others
have noted that the reversal of the gender gap that occurred
in the 1980s was mostly driven by White men and women.
Among Black men and women, women’s advantage emerged
much earlier than this; Black women’s rates of college
enrollment and completion exceeded Black men’s starting
with 1920s birth cohorts, and their advantage continued to
grow through the modern era (DiPrete & Buchmann, 2013;
McDaniel etal., 2011). Previous research focused on the set
of structurally based disadvantages facing Black boys and
men points to the group’s higher likelihood of being exposed
to factors such as exclusionary school discipline, policing,
and carceral facilities as factors limiting the group’s average
educational trajectories. In the education space, for example,
recent experimental research demonstrates that teachers are
more likely to punish Black boys than White boys for identi-
cal misbehavior (Owens, 2022).
Taking these two trends together, we highlight Black
women as a demographic group with structural overexpo-
sure to educational debt—what we might characterize as
a perverse consequence of their educational persistence.
Prior research has especially highlighted economically dis-
advantaged Black women’s predatory inclusion in higher
education via for-profit institutions. Cottom (2017) shows
how these institutions target women’s desire for financial
independence and encourage them to take on much more
debt than they can reasonably handle, only to offer them
weak training and poor economic returns. Others have con-
sidered how the financial burdens of higher education tend
to fall disproportionately on Black women who are rela-
tively advantaged. Research shows that Black girls with the
strongest academic credentials in high school—i.e., those
who are most likely to enter 4-year institutions and poten-
tially continue on to graduate school—have far fewer eco-
nomic resources than their similarly high-achieving peers
in other race-gender groups, including White boys, White
girls, and Black boys (Quadlin & Conwell, 2021). Thus, as
a result of Black women’s and girls’ tendency to academi-
cally out-perform their economic circumstances, they may
be disproportionately likely to enter costly institutions with
few financial resources.
We systematically consider how these generational and
race-gender patterns differ by students’ measured academic
test scores in adolescence. From a theoretical standpoint,
these test scores should be unambiguously positively related
to a range of attainment outcomes. The status attainment
model, for example, included achievement as a key predictor
of educational and occupational attainment (Sewell etal.,
1969, 1970; also see Blau & Duncan, 1967). Although the
relationship between youth test scores and adult economic
success may be straightforward for many individuals, such
models do not account for the high cost of higher education,
nor do they consider potential disparate returns to education
across race-gender groups. We thus consider our outcomes
across the range of test scores, guided by empirical findings
on Black women’s selection into higher education, as well
as their economic origins and outcomes. As many scholars
have demonstrated, standardized test scores measure both
ability and opportunity (Conwell, 2021; Neal & Johnson,
1996; Riegle-Crumb etal., 2019), with the latter in our
minds including how a child’s family, school, neighborhood,
healthcare, and other contexts facilitate or inhibit the expres-
sion of their innate capabilities via academic tests. Despite
these caveats, we follow other researchers in noting these
measures are also analytically useful because they are meas-
ured comparably across cohorts and are strongly correlated
with college attendance and completion and later life course
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

433Journal of Family and Economic Issues (2024) 45:430–443
1 3
financial outcomes. As we demonstrate, the educational
and economic payoffs to high test scores vary substantially
across cohorts and race-gender groups.
Data, Measures, andMethods Using Two
Strategically Timed Cohorts
We use data from two nationally representative datasets
representing two U.S. cohorts that have had very different
experiences in higher education and in their access to and
use of credit. The NLSY-79 is a survey of 12,686 respond-
ents born between 1957 and 1965 (we refer to them as “Baby
Boomers”). When they were first interviewed in the base
year of 1979, they were 14–22years old. Respondents were
interviewed annually through 1994 and biennially since
then. The NLSY-97 is a survey of 8984 respondents born
between 1980 and 1984 (we refer to them as “Millennials”).
When they were first interviewed in the base year of 1997,
they were 12–16years old. Respondents were interviewed
annually through 2011 and biennially since then.
Although these surveys were fielded during different time
periods, they share a focus on youth’s experiences in edu-
cation and the labor market, and they boast high retention
rates well into respondents’ adult years. The two surveys
also contain many of the same or comparable measures,
making them well-suited for our cross-cohort comparison
of educational attainment and economic outcomes. We
restrict both samples to four race-gender groups that also
constitute our main predictor variables: White men, White
women, Black men, and Black women. The main analyses
also stratify respondents according to standardized tests
that NLSY administered in both cohorts’ base year. In the
NLSY-79, this is the Armed Forces Qualifying Test (AFQT),
and in the NLSY-97, this is the Armed Services Vocational
Aptitude Battery (ASVAB), although we use only the aca-
demic subtests of the ASVAB in order to make this measure
functionally equivalent to the AFQT. Descriptive statistics
are shown in Table1. The analytic sample includes all White
and Black respondents with complete data on the AFQT/
ASVAB and our economic outcomes of interest (discussed
below), whether they attended college or not.1
We use four sets of outcome variables that gauge respond-
ents’ educational attainment and economic standing, which
we generally measure when respondents are age 35, as dis-
cussed further below. Educational attainment is measured as
respondents’ highest level of education completed. House-
hold income is measured as total net family income from
the previous calendar year. Household wealth is a composite
measure of all assets minus all debts. We assess wealth in
dollar amounts as well as, importantly, those who have no/
negative wealth versus positive wealth (0/1). Then, given
our focus on the economic consequences of higher education
expansion as well as inequality across the life course, we
constructed measures of cumulative student loans accrued.
These include student debt accrued for the respondent’s own
education (available for both cohorts) and student loans
accrued for the respondent’s children’s education (available
only for the NLSY-79 cohort because the NLSY-97 cohort
is too young). Notably, these are cumulative measures of
all student debt accrued for enrollment in higher education,
prior to taking into account any repayment.2 These differ
from some commonly used measures in the NLSY such as
“current debt” at age 35, which would be the amount remain-
ing to be repaid at age 35. Given prior research on Black
Table 1 Distributional data
on achivement test scores, by
cohort and race-gender group
Race-gender group n Achievement test scores
25th percentile 50th percentile 75th percentile
NLSY-79: trailing-edge baby boomers (AFQT)
White men 3541 28.31 53.76 77.78
White women 3502 31.15 53.19 75.36
Black men 1524 7.59 18.02 36.06
Black women 1504 8.88 19.49 36.51
NLSY-97: early millennials (ASVAB)
White men 1985 32.54 57.73 80.26
White women 1871 36.88 60.09 80.11
Black men 879 7.81 19.44 40.01
Black women 929 10.97 24.85 47.58
1 For the student debt analyses, a consequence of our comprehen-
sive sampling strategy is that some respondents accrue “0” in debt
because they did not attend college, while others accrue “0” while
attending college because their family can pay out of pocket. We also
analyze amounts of student debt among those with any debt, which
necessarily restricts this sample to college attendees. Both of these
samples and the implications of these samples are discussed in the
results.
2 For example, if a student accrued $10,000 for each of four years of
undergraduate education, their amount of student debt accrued at age
35 would be $40,000, even if they had already paid back their loans
in full.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

434 Journal of Family and Economic Issues (2024) 45:430–443
1 3
borrowers’ relatively high rates of default (Scott-Clayton &
Li, 2016), we might expect outstanding student loan bal-
ances at age 35 (which take into account repayment or lack
thereof) to be more even more stratified than what we show
here.
Outcomes measured at the household level are adjusted
for the number of household members and economies of
scale (i.e., divided by the square root of household size).
Research shows that household size adjustments are espe-
cially important when making intersectional comparisons,
in light of racial and gender differences in factors such as
marriage and non-marital partnering, number of resident
children, and extended family households (Marsh et al.,
2007). All dollar amounts are adjusted to 2017 dollars, cor-
responding to our most recent data.
We measure all outcomes (with one exception) at age 35
because this is the oldest age at which we can make com-
parisons across cohorts.3 In the most recent data release,
most of the NLSY-97 respondents were in their mid-thir-
ties. In addition, most respondents (though not all; Denice,
2017) will have completed their education by age 35. This
helps ensure that we have reasonably proxied lifetime edu-
cational attainment as well as all student debt respondents
accrue for their own education. Research shows that income
measured at this age is a reasonable proxy for lifetime earn-
ings (Haider & Solon, 2006). Wealth, however, typically
continues accumulating from the age we observe through
retirement, when it is spent down; events such as marriage,
divorce, and disability also influence wealth trajectories.
Prior research shows that wealth trajectories, exposure to
trajectory-altering events, and these events’ correlations with
wealth trajectories all vary by race and race-gender (Goda
& Streeter, 2021). In particular, scholars have highlighted
Black women’s high likelihood of trajectory-altering events
in mid-to-late life (Addo & Lichter, 2013; Brown, 2012).
Our analyses, therefore, capture Black and White women’s
and men’s wealth after some of these potential events, but
prior to others that may be consequential. In an exception
to the age 35 measurement period, we capture student loans
accrued for children up through the most recent data release,
regardless of respondent age. We do this to account for the
wide possible time horizon in terms of when respondents
could be investing in children’s education (and because there
is no need to cap respondent ages in order to make compari-
sons to the NLSY-97).
We use median regressions, logistic regressions, or mul-
tinomial logistic regressions, depending on the form of the
outcome variable and as specified in the figures. We include
our main predictor variables in regressions—including
race-gender and measured achievement, which is strongly
correlated with students’ likelihood of college attendance—
but otherwise show descriptive results in order to assess
respondents’ educational and economic circumstances as
observed.
Results
Educational Attainment
We begin by considering race-gender differences in educa-
tional attainment across the range of respondents’ test scores
and across cohorts. These results are shown in Fig.1. Given
the expansion of higher education that took place in the latter
half of the 20th century, we would expect educational attain-
ment to increase across cohorts. This is indeed what we find,
although the magnitudes of these increases are larger for
some race-gender groups than others. White men, for exam-
ple, historically have enjoyed relatively unrestricted access
to higher education (DiPrete & Buchmann, 2013), and thus
it follows that the differences in educational attainment we
observe across cohorts (conditional on test scores) are not
enormous. Across cohorts, White men at the median of test
scores were about equally likely to attain some college or
more. White men with the highest test scores became more
likely to attain more than a BA across cohorts—from about
a 0.45 predicted probability among Baby Boomers, to about
a 0.55 predicted probability among Millennials. Changes in
Black men’s educational attainment also are relatively mod-
est across cohorts. The patterns observed for the bottom 40
percent of the test score distribution, in particular, are highly
consistent for Black men Baby Boomers and Millennials.
Like what we saw for White men, Black men with the high-
est test scores became more likely to attain more than a BA
over time, moving from about a 0.55 predicted probability
to about 0.75—significantly higher than comparable White
men in both cohorts.
Perhaps unsurprisingly given research on the “rise of
women” in higher education (DiPrete & Buchmann, 2013),
the largest shifts across cohorts are among White and Black
women. The highest-achieving White women Baby Boomers
attended college fairly often, but even for those at the 80th
percentile of test scores, the most likely level of attainment
was only “some college.” For White women Millennials, on
the other hand, post-graduate education is the most likely
outcome at and above the 70th percentile of test scores.
Similarly, Black women’s educational attainment, condi-
tional on achievement, is relatively high in both cohorts;
even among Baby Boomers, the highest-achieving Black
women had about a 0.70 predicted probability of attain-
ing more than a BA. But among Black women Millennials,
even for those with only median test scores, the most likely
3 If respondents’ economic outcomes were missing at age 35, we
filled them in with amounts reported at ages 36–40 as available; for
the NLSY-97 cohort, the very oldest respondents were 38 in our data.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

435Journal of Family and Economic Issues (2024) 45:430–443
1 3
1979 Cohort 1997 Cohort
Fig. 1 Predicted probability of attaining a given level of education by age 35. Note: Multinomial logistic regressions; 95% confidence intervals
shown
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

436 Journal of Family and Economic Issues (2024) 45:430–443
1 3
outcome is to proceed beyond the BA. The highest-achiev-
ing Black women are virtually guaranteed to attain at least
some graduate-level education. Even the lowest-achieving
Black women have more than a 0.20 predicted probability
of attaining some college, and their predicted probabilities
of attaining a BA or more than a BA are both about 0.10.
Thus, while all race-gender groups’ educational attainment
has shifted upward to some extent across cohorts (condi-
tional on achievement), this was true especially for Black
women and, to a slightly lesser extent, White women.
Household Income
Figure2 shows race-gender differences in median house-
hold income across the range of test scores, adjusted for
inflation, household size, and economies of scale. Looking
across cohorts, we observe patterns of household income
that are more similar than they are different. All four race-
gender groups experience positive returns to achievement in
both cohorts, such that higher-achieving respondents have
higher household incomes than lower-achieving respond-
ents. Among Baby Boomers, the highest-achieving White
women tended to have lower median household incomes
than other race-gender groups—significantly lower than
White men (p < 0.05), Black women (p < 0.01), and Black
men (p < 0.001). Among Millennials, however, these gaps
are effectively closed, such that the highest achievers in
each race-gender group are not statistically distinguishable
in terms of their household income. (Yet, readers should
keep in mind that high-achieving members of these race-
gender groups have different educational attainment that
we have not controlled for—so, for example, Black women
and White men may have comparable median household
incomes conditional on achievement, but Black women at
this level of achievement are more likely to have attended
graduate school and incurred the costs to do so, as we saw
in Fig.1.)
The most notable shifts across cohorts are concentrated at
the bottom of the achievement distribution. In both cohorts,
the lowest-achieving Black women have lower household
incomes than members of other race-gender groups, but
this gap is most prominent in the Millennial cohort. This
is partly because the lowest-achieving White men’s house-
hold incomes are relatively high, with an adjusted median
of about $34,000/year for those at the 10th percentile of
achievement (versus about $11,000/year for similarly situ-
ated Black women; p < 0.001). White men ultimately experi-
ence the flattest slope across the range of achievement, but
because their intercept is so high, there is no point at which
White men are significantly disadvantaged relative to any
other race-gender group.
Household Wealth
In Fig.3, we begin to see the contours of how race-gender
inequality has deepened across cohorts. The top panel shows
respondents’ predicted probability of accumulating greater
than $0 in wealth—i.e., any wealth, of any amount greater
than zero—at the household level by the time they reach
age 35. In the Baby Boomer cohort, as seen in the top-left
panel, we observe large racial disparities particularly at the
bottom of the achievement distribution, with greater rela-
tive gender parity within racial groups. Among those at the
10th percentile of test scores, White men’s and women’s
households were about equally likely to hold greater than
$0 in wealth by the time they reached age 35, both with
a predicted probability of about 0.83. In contrast, Black
women’s predicted probability was 0.65, and Black men’s
was about 0.64 (both p < 0.001 compared to both White
men and women). White men’s and women’s chances of
Fig. 2 Adjusted household income at age 35. Note: median regressions; 95% confidence intervals shown
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

437Journal of Family and Economic Issues (2024) 45:430–443
1 3
having positive wealth continue to increase modestly across
the range of test scores, resulting in predicted probabilities
of 0.96 for White men and 0.95 for White women with the
highest achievement. The highest-achieving Black men are
statistically indistinguishable from White men and women,
with a predicted probability of 0.93. However, the highest-
achieving Black women’s chances of accumulating any
wealth at the household level are only about 0.89—signifi-
cantly lower than White men (p < 0.05) and White women
(p < 0.05) but comparable to Black men.
The bottom panel shows estimates for median household
wealth among those with positive wealth. Among those with
any household wealth in the Baby Boomer cohort, in the bot-
tom-left panel, we again observe large racial disparities cou-
pled with greater relative gender parity within racial groups.
Additionally, these racial gaps widen across the range of
achievement. The largest gap in terms of sheer point esti-
mates, for example, is between the highest-achieving White
women’s households (adjusted median of about $99,000)
and the highest-achieving Black men’s households (adjusted
median of about $44,000; p < 0.001).
Data from the Millennial cohort tell a different story. As
shown in the top-right panel, White men’s, Black men’s,
and White women’s households were about equally likely to
accumulate wealth across the range of achievement. We also
find significant contrasts between White men’s and wom-
en’s households, such that White men’s households were
more likely to accumulate wealth across virtually the entire
range of achievement (except for those with the very lowest
test scores; all others at least p < 0.05). Yet, we observe a
unique pattern among Millennial Black women, such that
their chances of accumulating any wealth drop steeply across
the range of achievement. Black women at the 10th percen-
tile of test scores have about a 0.77 predicted probability
of accumulating any wealth at the household level by age
35—significantly lower than White men’s (p < 0.001) and
Black men’s (p < 0.05) households, despite relatively equal
household incomes at this point in the achievement distri-
bution (see Fig.2). For Black women with the highest test
scores, their households’ chances of holding positive wealth
are only about 0.57. Put differently, and to reiterate, only
about half of the highest-achieving Black women Millenni-
als have any wealth at the household level by age 35. This is
by far the lowest point estimate across race-gender groups
and across cohorts.
The bottom-right panel, which shows estimates for
median wealth among Millennials (conditional on hold-
ing any wealth), is patterned similarly to what we saw in
(a) Predicted Probability of Having > $0 in Wealth
(b) Wealth, among Those with > $0 in Wealth
Fig. 3 Adjusted household wealth at age 35. Note: Logistic regressions in top panel; median regressions in bottom panel; 95% confidence inter-
vals shown
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

438 Journal of Family and Economic Issues (2024) 45:430–443
1 3
the Baby Boomer cohort. The largest disparities are again
between racial groups, and these gaps widen across the
range of respondents’ test scores. In an exception, we find
that White men’s households had greater wealth than White
women’s households at moderate levels of achievement,
between about the 30th and 70th percentile of test scores
(all at least p < 0.05). We otherwise observe relative gender
parity within the context of large racial gaps in wealth.4
Student Debt
We now turn to considering the extent to which the inci-
dence and amounts of student debt have changed across
cohorts for Black and White women and men. Figure4 sum-
marizes these results, showing respondents’ predicted prob-
abilities of accruing any student loans by age 35 (top panel)
as well as the cumulative amount of debt among those with
any student loans (bottom panel). We lift out three patterns
that are particularly consequential:
First, as with the results for wealth that we saw in
Fig.3, we observe large racial disparities, such that Black
women and men generally are more likely to hold stu-
dent debt than White women and men. However, unlike
what we saw with wealth, we find clear distinctions in
terms of gender as well, such that Black women generally
are more likely to hold student debt than Black men, and
White women generally are more likely to hold student
debt than White men. Black women clearly have the high-
est incidence of debt in both cohorts, but especially in the
Millennial cohort. Among NLSY-97 respondents at the
median of test scores, for example, Black women’s pre-
dicted probability of holding student debt was 0.66, versus
0.45 for Black men, 0.39 for White women, and 0.29 for
White men (all comparisons p < 0.001 relative to Black
women). These disparities, of course, are partly driven by
the fact that Black women Millennials with median test
scores have attained much more education than their coun-
terparts in other race-gender groups, as we saw in Fig.1.
But even among the highest-achieving respondents, who
are all virtually guaranteed to attain at least some higher
education, Black women still have the highest incidence of
student debt. Nearly all Black women with the highest test
scores are expected to accrue student debt by age 35, with
a predicted probability of 0.93, versus 0.85 for Black men
(a) Predicted Probability of Accruing Any Student Loans
(b) Amount of Student Loans Accrued, among Those with Student Loans
Fig. 4 Student loans accrued by age 35. Note: logistic regressions in top panel; median regressions in bottom panel; 95% confidence intervals
shown
4 When we use respondents’ relative place in the wealth distribution
as an outcome rather than specific dollar amounts, the look and pat-
terning of results is consistent with what we show in the main text.
The analyses using wealth percentiles tend to place greater empha-
sis on gaps between Black men and women in the Millennial cohort,
compared to what we show here.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

439Journal of Family and Economic Issues (2024) 45:430–443
1 3
(p < 0.05), 0.65 for White women (p < 0.001), and 0.60 for
White men (p < 0.001).
Second, across cohorts, the incidence of student debt
has been lifted upward for most race-gender groups at most
points in the test score distribution. This is true especially
for White and Black women. For example, among Baby
Boomers, White women with median test scores had a 0.20
predicted probability of accruing student debt. In the Millen-
nial cohort, this doubles to approximately 0.40. The upward
shift for Black women with median test scores is smaller by
comparison—from approximately 0.42 in the Baby Boomer
cohort to 0.65 in the Millennial cohort. Yet, considering that
Black women have the highest incidence of student debt at
most points in the test score distribution in both cohorts, it is
striking that their incidence has continued to grow at a high
rate across cohorts. White men are something of an excep-
tion, especially at the very top of the distribution, where
their probability of accruing student loans remains stable
across cohorts. Yet, we do see an upward shift among White
men with the lowest test scores. Their predicted probability
of holding debt is near-zero among Baby Boomers, as com-
pared to 0.12 among Millennials.
Third, the amounts of debt accrued (conditional on accru-
ing any debt) have stayed relatively consistent across cohorts
for two groups: White women and Black men. We observe
some minor shifts upward across the distribution of test
scores, but the slopes and general patterning are similar for
Baby Boomers and Millennials in both race-gender groups.
For the other two race-gender groups—Black women and
White men—their amounts of student debt in the Millennial
cohort (conditional on accruing any debt) are relatively con-
stant across the distribution of test scores, which is distinctly
not what is observed among Baby Boomers. With regard to
Black women in particular, this pattern is consistent with
research we discussed earlier on the predatory inclusion of
lower-resourced Black women at for-profit colleges (Cottom,
2017; Seamster & Charron-Chénier, 2017). These students
frequently do not complete their degree programs, and even
if they do, they experience weak or non-existent economic
returns; this is consistent with the patterns of education,
income, and debt we observe for Black women Millennials
with the lowest test scores. In contrast, rising debt among
low-achieving White men is not necessarily a pattern that
has been highlighted in the literature. Although the amount
of debt may be relatively high among this group, recall
that in the top panel, the incidence of student debt for low-
achieving White men is very low. In fact, when we consider
the median amount of debt among Millennials, inclusive of
those with zero values—as shown in Fig.5—we see that
low-achieving White men’s debt does not come close to
approaching that of low-achieving Black women.
Aside from student loans respondents accrue for their
own education, another consideration relevant to families’
financial lives is student loans respondents accrue for
their children’s education. As this point, only the NLSY-
79 respondents are old enough to have a critical mass of
children who have attended higher education, and thus we
Fig. 5 Student loans accrued by age 35—NLSY-97 cohort—inclusive
of those with $0 in student loans. Note: median regression; 95% con-
fidence intervals shown
(a)Predicted Probability of Accruing Any Student Loans for Children’s Education
(b)Amount Accrued, among Those with Any Student Loans for Children’s Education
Fig. 6 Student loans accrued for children’s education, NLSY-79
cohort. Note: logistic regression in top panel; median regression in
bottom panel; 95% confidence intervals shown
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

440 Journal of Family and Economic Issues (2024) 45:430–443
1 3
restrict our attention to the Baby Boomer cohort for these
analyses. Figure6 shows respondents’ predicted probability
of accruing student loans for their children’s education (top
panel) as well as the median amount accrued among those
with any of their children’s debt (bottom panel). Here, Black
women again emerge as uniquely overexposed to educational
debt, this time in an intergenerational fashion. As shown in
the top panel, the highest-achieving Black women have the
highest point estimate of all, with a 0.45 predicted probabil-
ity of accruing student debt for their children’s education
(n.s. compared to Black men but p < 0.01 compared to White
women and men). In the bottom panel, significant differ-
ences are much harder to parse, in part owing to a smaller
sample size and wide confidence intervals. The highest-
achieving Black women have the highest point estimate in
this figure, with median debt of about $26,000 (conditional
on accruing any student loans for children), although this
amount is not significantly higher than that of other race-
gender groups.
Given that our results point to high-achieving Black
women’s unique intergenerational exposure to student debt,
in Fig.7, we use a measure that combines data on respond-
ents’ own debt along with their children’s debt. Specifically,
we categorize respondents according to whether they: (1)
accrued no student debt for themselves or their children; (2)
accrued student debt for themselves, but not their children;
(3) accrued student debt for their children, but not them-
selves; or (4) accrued student debt for both themselves and
their children. The results reflect multiple dynamics includ-
ing, but not limited to, respondents’ chances of attending
college, their chances of having children, their children’s
chances of attending college, and their chances of accruing
student debt in their and their children’s interfacing with
higher education. But while the mechanisms giving rise to
these patterns are complex, they nonetheless clearly capture
race-gender disparities in exposure to student debt in inter-
generational perspective.
As an illustration of this point, consider those in category
(1), who never accrued student debt across the life course.
Toward the bottom of the test score distribution, respond-
ents presumably are not accruing debt because they and
their children are not attending college. Accordingly, it is
not necessarily surprising that these respondents have not
accrued any student debt across the life course. Toward the
top of the test score distribution, however, respondents (and
their children, if they have any) are virtually guaranteed to
attend college—thus, race-gender disparities in this loca-
tion reflect respondents’ multi-generational ability to pay for
higher education out of pocket, without having to accrue any
debt either as a student or as a parent.5 Indeed, we find that
Fig. 7 Incidence of student loans for self and/or children, NLSY-79
cohort. Note: multinomial logistic regression; 95% confidence inter-
vals shown. For each race-gender group, we show the predicted prob-
ability of respondents accruing student loans for: (1) neither them-
selves nor their children; (2) themselves, but not their children; (3)
their children, but not themslves; and (4) both themselves and their
children
5 Based on the NLSY coding scheme, if a respondent’s child accrued
student debt in their name, respondents could still be sorted into cat-
egory (1) as long as they did not accrue any parent loans. We suspect
this is relatively uncommon among those with the highest test scores
in category (1), but it is a possibility nonetheless.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

441Journal of Family and Economic Issues (2024) 45:430–443
1 3
the highest-achieving White men (0.26) and White women
(0.31) are much more likely to never accrue student debt,
relative to their Black men (0.12) and Black women (0.09)
counterparts (all comparisons between White and Black
respondents p < 0.001).
Category (4) similarly requires a dynamic interpreta-
tion—these are respondents who accrued debt both as a
student and as a parent. Again, toward the bottom of the test
score distribution, respondents presumably are not accru-
ing debt because they and their children are not attending
college. But toward the top of the test score distribution,
respondents and their children presumably are attending
college, and thus race-gender disparities in this location
reflect respondents’ multi-generational exposure to student
debt. This is where Black women emerge as overexposed
relative to other race-gender groups. Their predicted prob-
ability of accruing student loans for both themselves and
their children is 0.36—far higher than that of Black men
(0.14; p < 0.05), White women (0.12; p < 0.01), and White
men (0.11; p < 0.01). Thus, more than any other race-gender
group at any other location in the distribution of test scores,
the highest-achieving Black women Baby Boomers have
been uniquely likely to continually interface with student
loans across the life course.
Conclusion
Using data from Black and White women and men in two
recent cohorts, this article has considered how similarly
situated students (as measured by their achievement in ado-
lescence) fared in terms of educational attainment and eco-
nomic outcomes mostly assessed at age 35. These are two
cohorts that, despite being born only a few decades apart,
experienced vastly different landscapes in terms of higher
education opportunity, cost, and access to credit. While the
analyses reveal multiple contours in how students and fami-
lies have interfaced with higher education across the 20th
century, some of the most striking findings pertain to high-
achieving Black women’s structural overexposure to student
debt. Our findings indicate that student debt is a near-modal
life course experience for high-achieving Black women, and
that intergenerational experiences with student debt also are
highly common. We see this as an example of how structural
inequalities by both race and gender harm those who, by vir-
tue of ability and opportunity, are best poised for economic
mobility through education. This is a trend that has been
demonstrated through research on how racial inequalities
“harm the best” in K-12 education (Hanushek & Rivkin,
2009; Riegle-Crumb & Grodsky, 2010) but that has not, to
our knowledge, been extended into research on higher edu-
cation and the economic life course. Future research should
continue to examine these processes, and especially should
follow the Millennial cohort as they approach mid-life and
the “student debt generation” potentially accrues yet more
debt for their children’s education.
Funding NQ received funding for this article from the Russell Sage
Foundation and the UCLA Academic Senate
Data Availability The National Longitudinal Surveys of Youth are pub-
licly available through the Bureau of Labor Statistics: https:// www.
bls. gov/ nls/.
Declarations
Conflict of interest The authors declare that they have no conflicts of
interest.
Ethical approval This study uses publicly available data from the
NLSY-79 and NLSY-97.
Informed consent All respondents gave their informed consent to par-
ticipate in the National Longitudinal Surveys of Youth.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
References
Addo, F. R., & Lichter, D. T. (2013). Marriage, marital history, and
black—white wealth differentials among older women. Journal
of Marriage and Family, 75(2), 342–362. https:// doi. org/ 10. 1111/
jomf. 12007
Addo, F. R., Houle, J. N., & Simon, D. (2016). Young, black, and (still)
in the red: Parental wealth, race, and student loan debt. Race and
Social Problems, 8, 64–76.
Becker, G. (1964). Human capital. National Bureau of Economic
Research.
Blau, P. M., & Duncan, O. D. (1967). The American occupational
structure. Wiley.
Bonilla-Silva, E. (1997). Rethinking racism: Toward a structural inter-
pretation. American Sociological Review, 62(3), 465–480.
Bowen, W. G., Chingos, M. M., & McPherson, M. S. (2009). Crossing
the finish line: Completing college at America’s Public Universi-
ties. Princeton University Press.
Brown, T. (2012). The intersection and accumulation of racial and
gender inequality: Black women’s wealth trajectories. The Review
of Black Political Economy, 39(2), 239–258. https:// doi. org/ 10.
1007/ s12114- 011- 9100-8
Buchmann, C., & DiPrete, T. A. (2006). The growing female advantage
in college completion: The role of family background and aca-
demic achievement. American Sociological Review, 71, 515–541.
https:// doi. org/ 10. 1177/ 00031 22406 07100 401
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

442 Journal of Family and Economic Issues (2024) 45:430–443
1 3
Carnevale, A. P., Rose, S. J., & Cheah, B. (2013). The college payoff:
Education, occupations. Georgetown University Center on Educa-
tion and the Workforce.
Cepa, K. (2021). Financing adulthood: The risks and hopes of parent
borrowing through the parent loans for undergraduate students
(PLUS) program [Ph.D., University of Pennsylvania]. https://
www. proqu est. com/ docvi ew/ 25434 12425/ abstr act/ 90F46 E8779
38482 6PQ/1
College Board. (2019). Trends in college pricing 2019 highlights.
https:// resea rch. colle geboa rd. org/ trends/ colle ge- prici ng/ highl ights
Conwell, J. A. (2021). Diverging disparities: Race, parental income,
and children’s math scores, 1960 to 2009. Sociology of Educa-
tion, 94(2), 124–142. https:// doi. org/ 10. 1177/ 00380 40720 963279
Cottom, T. M. (2017). Lower ed: The troubling rise of for-profit col-
leges in the new economy. The New Press.
Crenshaw, K. (1989). Demarginalizing the intersection of race and
sex: A black feminist critique of antidiscrimination doctrine,
feminist theory and antiracist politics. University of Chicago
Legal Forum, 139. https:// heino nline. org/ HOL/ Landi ngPage?
handle= hein. journ als/ uchcl f1989 & div= 10& id= & page=
Dale, S. B., & Krueger, A. B. (2002). Estimating the payoff to attend-
ing a more selective college: An application of selection on
observables and unobservables. Quarterly Journal of Econom-
ics, 117, 1491–1527.
Denice, P. (2017). Back to school: Racial and gender differences
in adults’ participation in formal schooling, 1978–2013.
Demography, 54(3), 1147–1173. https:// doi. org/ 10. 1007/
S13524- 017- 0570-6
DiPrete, T. A., & Buchmann, C. (2013). The rise of women: The
growing gender gap in education and what it means for Ameri-
can schools. Russell Sage Foundation.
Dwyer, R. E. (2018). Credit, debt, and inequality. Annual Review
of Sociology, 44, 237–261. https:// doi. org/ 10. 1146/ annur
ev- soc- 060116
Dwyer, R. E., McCloud, L., & Hodson, R. (2012). Debt and gradua-
tion from American universities. Social Forces, 90, 1133–1155.
https:// doi. org/ 10. 1093/ sf/ sos072
Fletcher, C., Webster, J., & Di, W. (2020). PLUS borrowing in Texas.
Trellis Research. https:// www. trell iscom pany. org/ wp- conte nt/
uploa ds/ 2020/ 01/ parent- plus- borro wing- study. pdf
Gaddis, S. M. (2015). Discrimination in the credential society: An
audit study of race and college selectivity in the labor mar-
ket. Social Forces, 93, 1451–1479. https:// doi. org/ 10. 1093/ sf/
sou111
Goda, G. S., & Streeter, J. L. (2021). Wealth trajectories across
key milestones: Longitudinal evidence from life-course transi-
tions (Working Paper No. 28329). National Bureau of Economic
Research. https:// doi. org/ 10. 3386/ w28329
Goldin, C., Katz, L. F., & Kuziemko, I. (2006). The homecoming of
American college women: The reversal of the college gender
gap. Journal of Economic Perspectives, 20(4), 133–156. https://
doi. org/ 10. 1257/ jep. 20.4. 133
Gross, J. P. K., Cekic, O., Hossler, D., & Hillman, N. (2009). What
matters in student loan default: A review of the research litera-
ture. Journal of Student Financial Aid, 39(1), 19–29.
Haider, S., & Solon, G. (2006). Life-cycle variation in the asso-
ciation between current and lifetime earnings. American Eco-
nomic Review, 96(4), 1308–1320. https:// doi. org/ 10. 1257/ aer.
96.4. 1308
Hanushek, E. A., & Rivkin, S. G. (2009). Harming the best: How
schools affect the black-white achievement gap. Journal of
Policy Analysis and Management, 28(3), 366–393. https:// doi.
org/ 10. 1002/ pam. 20437
Houle, J. N. (2014a). A generation indebted: Young adult debt across
three cohorts. Social Problems, 61, 448–465.
Houle, J. N. (2014b). Disparities in debt: Parents’ socioeconomic
resources and young adult student loan debt. Sociology of Edu-
cation, 87, 53–69. https:// doi. org/ 10. 1177/ 00380 40713 512213
Houle, J. N., & Addo, F. R. (2019). Racial disparities in student debt
and the reproduction of the fragile black middle class. Sociology
of Race and Ethnicity, 5(4), 562–577. https:// doi. org/ 10. 1177/
23326 49218 790989
Houle, J. N., & Addo, F. R. (2022). A dream defaulted: The student
loan crisis among black borrowers. Harvard Education Press.
Hout, M. (2011). Social and economic returns to college education
in the United States. Annual Review of Sociology, 38, 379–400.
https:// doi. org/ 10. 1146/ annur ev. soc. 012809. 102503
Jackson, B. A., & Reynolds, J. R. (2013). The price of opportunity:
Race, student loan debt, and college achievement. Sociological
Inquiry, 83, 335–368.
Lemieux, T. (2006). Postsecondary education and increasing wage
inequality. American Economic Review, 96, 195–199.
Marsh, K., Darity, W. A., Jr., Cohen, P. N., Casper, L. M., & Salters,
D. (2007). The emerging black middle class: Single and living
alone. Social Forces, 86(2), 735–762. https:// doi. org/ 10. 1093/
sf/ 86.2. 735
Martin, E. C., & Dwyer, R. E. (2021). Financial stress, race, and
student debt during the great recession. Social Currents, 8(5),
424–445. https:// doi. org/ 10. 1177/ 23294 96521 10266 92
Massey, D. S., Charles, C. Z., Lundy, G. F., & Fischer, M. J. (2003).
The source of the river: The social origins of freshmen at Amer-
ica’s selective colleges and universities. Princeton University
Press.
McDaniel, A., DiPrete, T. A., Buchmann, C., & Shwed, U. (2011).
The black gender gap in educational attainment: Historical
trends and racial comparisons. Demography, 48, 889–914.
https:// doi. org/ 10. 1007/ s13524- 011- 0037-0
Neal, D. A., & Johnson, W. R. (1996). The role of premarket factors
in Black-White wage differences. Journal of Political Economy,
104(5), 869–895. https:// doi. org/ 10. 1086/ 262045
Nunley, J. M., Pugh, A., Romero, N., & Seals, R. A. (2015). Racial
discrimination in the labor market for recent college graduates:
Evidence from a field experiment. The B.E. Journal of Eco-
nomic Analysis & Policy, 15(3), 1093–1125. https:// doi. org/ 10.
1515/ bejeap- 2014- 0082
Owens, J. (2022). Double jeopardy: Teacher biases, racialized organ-
izations, and the production of racial/ethnic disparities in school
discipline. American Sociological Review, 87(6), 1007–1048.
https:// doi. org/ 10. 1177/ 00031 22422 11358 10
Pyne, J., & Grodsky, E. (2020). Inequality and opportunity in a per-
fect storm of graduate student debt. Sociology of Education,
93(1), 20–39. https:// doi. org/ 10. 1177/ 00380 40719 876245
Quadlin, N., & Conwell, J. A. (2021). Race, gender, and parental
college savings: Assessing economic and academic factors.
Sociology of Education, 94(1), 20–42. https:// doi. org/ 10. 1177/
00380 40720 942927
Quadlin, N., & Powell, B. (2022). Who should pay? Higher educa-
tion, responsibility, and the public. Russell Sage Foundation.
Riegle-Crumb, C., & Grodsky, E. (2010). Racial-ethnic differences
at the intersection of math course-taking and achievement. Soci-
ology of Education, 83(3), 248–270. https:// doi. org/ 10. 1177/
00380 40710 375689
Riegle-Crumb, C., King, B., & Irizarry, Y. (2019). Does STEM stand
out? Examining racial/ethnic gaps in persistence across postsec-
ondary fields. Educational Researcher, 48(3), 133–144. https://
doi. org/ 10. 3102/ 00131 89X19 831006
Scott-Clayton, J., & Li, J. (2016). Black-white disparity in student
loan debt more than triples after graduation. Brookings Evi-
dence Speaks Reports, 2(3). https:// www. brook ings. edu/ wp-
conte nt/ uploa ds/ 2016/ 10/ es_ 20161 020_ scott- clayt on_ evide
nce_ speaks. pdf
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

443Journal of Family and Economic Issues (2024) 45:430–443
1 3
Seamster, L., & Charron-Chénier, R. (2017). Predatory inclusion
and education debt: Rethinking the racial wealth gap. Social
Currents, 4(3), 199–207. https:// doi. org/ 10. 1177/ 23294 96516
686620
Sewell, W. H., Haller, A. O., & Portes, A. (1969). The educational
and early occupational attainment process. American Sociologi-
cal Review, 34, 82–92.
Sewell, W. H., Haller, A. O., & Ohlendorf, G. W. (1970). The edu-
cational and early occupational status attainment process:
Replication and revision. American Sociological Review, 35,
1014–1027. https:// doi. org/ 10. 2307/ 20933 79
Snyder, T. D., de Brey, C., & Dillow, S. A. (2019). Digest of Educa-
tion Statistics 2017. Institute of Education Sciences.
United States Census Bureau. (2017). Levels of education: Percent-
age of the population completing high school bachelor’s or
higher. https:// www. census. gov/ libra ry/ visua lizat ions/ 2017/
comm/ educa tion- bache lors. html
Zaloom, C. (2019). Indebted: How families make college work at any
cost. Princeton University Press.
Publisher's Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com