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. Author manuscript; available in PMC: 2024 Nov 27.
Published in final edited form as: Emerg Adulthood. 2024 May 29;12(5):878–893. doi: 10.1177/21676968241241560

Unequally Indebted: Debt by Education, Race, and Ethnicity and the, Accumulation of Inequality in Emerging Adulthood

Rachel E Dwyer 1,*, Laura M DeMarco 2
PMCID: PMC11600330  NIHMSID: NIHMS2013949  PMID: 39605803

Abstract

Emerging adults in the U.S. face significant economic uncertainty during the early life course. Economic uncertainties grew in the 2000s, especially for the Millennial cohort. Access to credit can be a resource to manage the instability that characterizes emerging adulthood. However, debt can also become a burden, making credit like a “double-edged sword.” We study inequality in debt holding for five debt types that provide distinct resources and burdens, including mortgages, car loans, student loans, credit cards, and other debts to businesses. We analyze the extent to which the Millennial cohort accumulated unequal debts by the end of emerging adulthood using the National Longitudinal Survey of Youth, 1997 Cohort. We find strikingly unequal debt holding by education, race/ethnicity, and education-by-race/ethnicity for Millennial emerging adults. We conclude that policies and programs that support emerging adult financial wellbeing will be crucial for healthy development and reduced inequalities during this life course stage.

Jeffrey Arnett’s theory of emerging adulthood identified a new developmental life stage between adolescence and young adulthood in contemporary societies (Arnett 2000, 2004). Societies with an emerging adulthood stage provide greater fluidity in the years after adolescence than do traditional societies where pathways to adulthood are more defined. Emerging adults aged 18 to 29 engage in a period of exploration as they move into adult roles, resulting in higher rates of tertiary education and later transitions to marriage and parenting than in traditional societies. Core experiences associated with this longer transition to adult roles fall along five key dimensions: identity explorations, instability, self-focus, feeling in-between, and possibilities/optimism (Arnett 2004). These dimensions shape emerging adult development and well-being, including financial well-being. Exploring the possibilities of emerging adulthood entails significant financial costs, and finances can be a source of instability, feeling in-between, and optimism (Vosylis and Klimstra 2022). Financial demands became even more important for the Millennial cohort coming of age in the early 2000s, when economic insecurity and indebtedness increased significantly (Dwyer et al. 2011). Thus, financial demands have significant implications for development, well-being, and inequality among emerging adults (Cherney et al. 2020; Burcher et al. 2021; Vosylis and Klimstra 2022).

Access to credit and the taking on of debt can be a resource to manage the financial demands of emerging adulthood (Vosylis and Klimstra 2022). At the same time, debt can become a burden that limits capacity to achieve adult milestones (Nau et al. 2015; Bowen et al. 2021). This “double-edged sword” feature of credit (Dwyer et al. 2012) captures some of what Larry Nelson (2021) calls the affordances provided by the US environment of emerging adulthood: access to credit may help emerging adults take full advantage of opportunities for identity exploration, self-focus, and exploring possibilities for the future. But the resulting debt may also contribute to the instability and feeling in-between dimensions and may constrain later options. There has been less attention to financial inequality than to other forms of inequality among emerging adults, but there is increasing concern as evidence mounts that Millennials face significant economic uncertainty (Arnett 2016; Furstenberg 2016; Silva 2016). We contribute to the developing consensus that financial pressures are a critical crucible of unequal emerging adulthoods (Cherney et al. 2020; Burcher et al. 2021; Vosylis and Klimstra 2022).

We study the descriptive pattern of debt holding using the National Longitudinal Survey of Youth 1997 Cohort (NLSY97), which follows a cohort of Millennials born in the early 1980s. The Millennial cohort came of age during a time of broad credit access but also growing economic insecurity. The US federal government reduced regulation of financial markets as a bid to reduce conflict over redistribution by increasing credit access but during a time of growing state austerity (Krippner 2012). These changes fell particularly hard on the Millennial cohort who were coming of age during the time of both increasing credit access and declining state support for emerging adults (Lin and Neely 2020). Lower state subsidies for higher education during this period occurred just as a bachelor’s degree became increasingly important for securing a decent living, and as a result the Millennial cohort saw growing student debt (Cherney et al. 2020; Lin and Neely 2020). Millennials also faced broader financial challenges as they navigated the milestones of emerging adulthood, leading to greater reliance on unsecured debt and challenges in transitioning to adult roles such as homeownership (Houle 2014; Silva 2013; DeLuca et al. 2016).

We ask whether and to what extent Millennial emerging adults accumulated unequal debts by the end of emerging adulthood? We compare advantaged and disadvantaged emerging adults across two of the most important dimensions of inequality in emerging adulthood in the US: educational attainment and race/ethnicity. Understanding unequal debts is important in order to understand the stressors of emerging adulthood in the US and other financialized contexts. Unequal debts both reflect differences in financial well-being during emerging adulthood and produce inequalities in later life financial well-being based on differential experiences of emerging adulthood.

The Accumulation of Inequality in Emerging Adulthood for Millennials

We study unequal debts at the end of emerging adulthood as the accumulated result of the developmental challenges of the second decade of the life course for the Millennial cohort. We first develop our theory of unequal debts, and then develop specific expectations by educational attainment, race/ethnicity, and education-by-race/ethnicity.

Unequal Debts: The Double-Edged Sword and Linear and Nonlinear Inequalities

Many types of inequalities fall along a linear distribution with resources associated with greater advantage, including for example income and net worth. Understanding inequality in debt presents a puzzle, however, because it requires considering nonlinearities. The reason is debt-holding typically results from both access to credit as well as the need for credit—and both are unequally distributed (Dwyer 2018; Maroto 2021). Some types of debt for emerging adults likely do follow a linear pattern, such that more advantaged emerging adults have greater access to credit for investment in property such as mortgages relative to less advantaged emerging adults who may rely on credit mainly for emergencies such as payday loans. However, we propose that the intersection of access and need likely also produces nonlinearities such that those with middling levels of advantage are most likely to take on some forms of debt. The reason is that those with middling advantage have more access to credit than less advantaged peers, but more need for credit than more advantaged peers. Moreover, debt types differ in the balance between resource and burden, with some debts providing a greater likelihood of contributing to financial well-being than others. Extending the metaphor of the double-edged sword, some debt types have sharper edges on the resource side while others have sharper edges on the burden side. Which edge is sharpest is in turn significantly determined by the difficulty of access to the credit and the source of the need for credit.

We therefore expect that distinct debt types will produce different patterns of association with advantage versus disadvantage depending on that doubled-edge balance of access and need. Home mortgages have property as collateral, but the application process involves intensive scrutiny of capacity to repay. We therefore expect a positive linear association of housing debt with advantage. Car loans can also be an investment in economic security and mobility and the application process for car loans is also relatively intensive. However, the used car market provides wide access to relatively inexpensive cars and loans while the most advantaged may receive cars as gifts or be able to pay in cash. We expect a nonlinear associations of car loans with advantage, with greater likelihood of holding car loans for emerging adults with middling advantage. Student loans are available to almost all enrolled in a tertiary school so long as they submit a FAFSA, but the most advantaged may be able to avoid student loans given greater family resources. We expect a nonlinear association where student loans are held most by emerging adults with middling advantage. Credit cards provide short-term resources without being secured by property and charge higher interest rates and penalties, but all require an application and some evidence of financial stability. Given the barriers to access combined with the costs of attainment, we expect a nonlinear association of greater holding of credit cards among those with middling advantage. Debts outside mainstream credit markets such as payday loans or past due bills may accumulate for emerging adults who face financial emergencies but lack access to lower-cost credit. These alternative debts are highly accessible—with a minimal credit check or externally imposed—but only arise out of severe financial need. We therefore expect a negative linear association with advantage for debts outside mainstream credit markets.

In sum, we expect that linear associations with advantage are most likely for the debts with the most and least opportunity for positive outcomes: mortgages versus debts outside mainstream credit markets. We expect nonlinear associations, however, are most likely for the attainment and consumption debts: car loans, student loans, and credit cards.

Unequal Debts by Education, Race/Ethnicity, and Education-by-Race/Ethnicity

We have framed advantage in general terms, but educational and race/ethnic inequality intersect with financial inequalities in different ways relative to credit access and need for credit. Given the central importance of credit access, we focus on the likelihood of debt-holding. We also analyze inequality in debt levels but expect fewer inequalities net of unequal debt holding.

Unequal Debts by Education.

Education has the potential to protect from some risky types of debt holding via knowledge and resources, while uniquely exposing young adults to other forms of debt including student loans. Many emerging adults enroll in college at some point, but significant inequality in attainment develops by the end of emerging adulthood: Some earn a bachelor’s degree or beyond, others earn an associate degree, but many who enroll never earn a degree (McMillan Cottom 2017). We expect the general patterns of association of unequal debts for advantage and disadvantage explained above will hold for unequal educational attainment. First, we expect linear patterns of association between education and the most advantageous mortgage debt as well as between education and the least advantageous debts outside mainstream credit markets. Specifically, we expect high-attainment emerging adults with bachelor’s or advanced degrees are more likely to take on mortgages and less likely to take on debts outside mainstream credit markets. We expect low-attainment emerging adults with no college are likely to have the inverse pattern in being less likely to take on debt secured by property and more likely to take on debts outside mainstream credit markets. However, the costs of higher education (including costs of attending, cost of living, and opportunity costs) mean that gaining attainment is itself financially demanding (Terriquez and Gurantz 2015). Thus, we expect nonlinear patterns for unsecured debts such that emerging adults with some college, an associate degree, or a bachelor’s degree, may be most likely to carry car loans, student loans and credit card debt relative to those with the highest and lowest educational attainment because the middling attainment groups have more access to credit than the most disadvantaged, but more need to support their attainment than the most advantaged.

Unequal Debts by Race/Ethnicity.

Systemic racism in the form of residential segregation and discrimination in financial markets has resulted in particularly low credit access in financial markets for Black populations (Bonilla-Silva 1997, 2021; Norris 2023). Segregation and discrimination have also reduced credit access for Hispanic populations, but to a lesser extent than for Black populations given the history of anti-Black racism and US slavery (Bonilla-Silva 1997; Loya 2022). The Hispanic population is diverse in racial identification and nativity and includes more advantaged groups with greater credit access including a sizable group identifying as White US citizens (McConnell 2015; Gullickson 2019; Blake 2019). Systemic racism may also lead to other race/ethnic differences in financial attitudes and behavior, including for example more risk aversion among Black emerging adults that may lead to lower levels of debt-holding (Rucks-Ahidiana 2022). We expect that the combination of lower credit access with higher need will result in Black emerging adults having a lower likelihood of carrying all financial market debts including mortgages, car loans, and credit cards, and other debts: in other words, we expect a positive linear association between racial status advantage and debt-holding with White emerging adults the most likely to hold debt, Black emerging adults the least likely to hold debt, and Hispanic emerging adults in the middle. We expect student debt will be the exception and show a nonlinear pattern of higher debt-holding among Black and Hispanic emerging adults because it is mainly distributed by the state through the federal student loan system and thus provides higher access than racial unequal financial markets (Addo et al. 2016). Scholars have highlighted the contribution of the racial wealth gap to this student loan holding in producing greater need for student loans among Black students due to lower family resources (Houle and Addo 2022). Our approach to studying all the most common youth debt together reveals another potential factor, also driven by systemic racism but having more to do with exclusion from credit markets: the distinctively open access of student loans compared to other types of debt. Black emerging adults may be particularly dependent on student loans relative to Hispanic emerging adults due to lower access to other forms of credit.

Unequal Debts by the Interaction of Education and Race/Ethnicity.

The interaction of educational attainment with race/ethnicity raises the puzzle of whether educational attainment protects Black and Hispanic emerging adults from financial market exclusion. Black emerging adults increasingly enroll in college and the Black middle class has grown substantially over time through the enormous anti-racist efforts of Black families and social movements led by Black populations (Addo et al. 2016). Yet even highly educated Black adults have fewer family resources on which to rely relative to White and to a lesser extent Hispanic families (Houle and Addo 2019). Systemic racism also appears to reduce the effectiveness of educational attainment for financial market inclusion (Wherry et al. 2019; Lin and Neely 2020; Bonilla-Silva 2021). Therefore, we expect different patterns of nonlinear associations for distinct racial groups. We expect the pattern of association between education and debt proposed above occur mainly for White emerging adults. We expect a different pattern of association will hold for Black emerging adults due to the large racial wealth gap and other sources of exclusion due to systemic racism. We expect education will be less effective for avoiding debt associated with consumption and emergencies such as credit cards and other debts outside mainstream credit markets for Black compared to White emerging adults. As a result, we expect even highly educated Black emerging adults will have a similar likelihood of carrying student loans, credit card, and other credit market debt types as Black emerging adults with middling educational attainment. We expect similar patterns for Hispanic emerging adults experience compared to Black because the interaction with education likely captures some of the variation with the Hispanic population. However, Hispanic emerging adults may instead fall more in the middle of the White and Black pattern because diversity in experiences within that population leads to a range of experiences with racism even when differentiated by education (Nembhard and Chiteji 2006; Ovink 2016).

Method

Participants

We study debt by education, race/ethnicity, and education-by-race/ethnicity for a Millennial cohort of emerging adults using the National Longitudinal Survey of Youth, 1997 Cohort (BLS 2019). The survey follows 8,984 participants born in the early 1980s, with interviews occurring annually from 1997 to 2011 and biennially after that. The NLYS97 data are uniquely suited to answer our questions as the survey asks about debt holding and education and oversamples Black and Hispanic respondents. The interviews include questions about debt when respondents were first considered independent and at specific ages in five-year increments, including age thirty. Between 2010 and 2017, all participants reached eligibility for the Assets 30 module, the focus of this analysis. We focus on age thirty as the end of the period of emerging adulthood to understand inequalities that have accumulated over the life stage and as the cohort moves into mature adulthood. The Assets 30 module also provides the most detailed and complete measures of debt that all participants completed to date. We conducted supplemental analyses of the debt measures at age twenty-five. We find little holding of mortgage debt as most moves to homeownership occur after age twenty-five, though similar patterns on the other four debt types. Thus, the assets thirty module is the first round with information on all the most common types of debt entered into by emerging adults. We draw on all years to construct variables as needed.

Missing data.

Our main analytic sample includes 7,275 respondents with complete data from the age thirty interview. While 7,710 participants received the Assets 30 module, we excluded 146 cases who were missing data on one or more types of debts to ensure sample comparability across models. We further limit the analytic sample to those identifying as non-Hispanic Black, non-Hispanic White, or Hispanic (n=7,297)—those identifying as multiple or other races were excluded from the analysis because of the small sample size. Finally, a small number of respondents were excluded from the sample because of missing data on cohabitation/marriage (n=22). We forward fill responses from earlier waves for missing data, increasing validity in longitudinal data where possible.

Measures

Debt holding.

First, we measure debt holding with dichotomous indicators coded as 1 if the respondent carries a debt type. The NLSY97 asks respondents about money currently owed in distinct categories of debt. Housing debt includes mortgages and loans using the property as collateral, such as home equity lines of credit. Car debt refers to any money owed on a car or other vehicle. Student debt refers to money owed on educational loans from federal aid or private lenders. Credit card debt includes money owed on all open and closed credit cards. Finally, other debts to businesses captures money owed “to any other businesses, such as stores, doctor’s offices, hospitals, or banks. Please include any installment plans, rent-to-own accounts, or any other business that you owe money to.” We expect respondents may also report some debt to the state here given that it is the only residual “other debt” category (DeMarco et al. 2021). The category captures both alternative financial services debts and past-due debts.

Amount of debt held.

We capture the amount of money currently owed for each debt type described above. Respondents were asked to report the amount of debt owed at the time of the interview. If respondents were unsure, they entered a range from which we interpolated the median value. All values were standardized to 2019 dollars using the Consumer Price Index.

Education.

We measure the highest educational attainment at the end of emerging adulthood by the age thirty interview. In our first set of analyses, we measure education in five categories: no college enrollment; some college but no degree; associate degree; bachelor’s degree (BS or BA); and advanced degree (master’s, PhD, or professional degree). In models interacting race with education, we measure education in three categories to maintain a sufficient sample size: no college degree, associate degree, and bachelor’s degree or more.1

Race/ethnicity.

We measure race/ethnicity with respondent self-reports. Separate questions collect racial and Hispanic ethnicity identification, and our focal groups are non-Hispanic Black, non-Hispanic White, and Hispanic of all races.2 We use the shorthand Black, White, and Hispanic and capitalize all as reflecting socially constructed concepts based on unequal histories of racist oppression. We use the term Hispanic as the concept used in the NLSY97. Because the sample sizes are smaller for the Hispanic population, our conclusions for this group are somewhat more tentative than for the other groups. We discuss all other racial groups and those who select multiple races descriptively but cannot include these groups in our main analyses because of small sample sizes.

Control variables.

We analyze a parsimonious model and avoid controlling for mechanisms that may suppress associational patterns, consistent with our goal of describing the landscape of debt holding at the end of emerging adulthood. We control for cohabitation/marital status using a four-category variable of whether the participant was never married (the reference category), currently cohabiting, currently married, or separated/divorced/widowed (and not cohabiting). We also include a dichotomous indicator coded as 1 if the respondent had any children. These demographic transitions may affect the association between education, race, and particular types of debt holding, given that family formation is linked to various forms of investment and consumption (Addo et al. 2019). We account for the respondent’s residential location, including region (North Central, South, and West, with North East as the reference category), and a binary indicator of living in an urban area. Both may affect the role of education and race in access to credit and labor market resources and returns (Faber 2019; Rhodes 2022). We account for the proportion of weeks since the last interview during which the respondent was employed, which affects the association of debt by education and race. We control for age at the time of the interview because credit access tends to increase across the life course and thus may affect the relationship between debt-holding, education and race (Houle 2014). We control for sex as women have on average higher educational attainment in the US, and thus may affect our core relationships (Dwyer et al. 2012).

Summary.

Table 1 reports weighted summary statistics reported by education and race/ethnicity, with columns for each debt type. 3

Table 1.

Descriptive Statistics on Debt Holding, by Education and Race/Ethnicity

House debt Car debt Student debt Credit Card Other debt
Total
(n=7541)		 Prop. holding debt 0.29 0.38 0.27 0.329 0.21
Median $147,753 $13,169 $20,458 $3,000 $2,000
Weighted Mean $166,955 $16,781 $31,351 $5,639 $10,804
Weighted SE 2269 284 916 168 1095
Degree No College
(n=2694)		 Prop. holding debt 0.17 0.28 0.01 0.19 0.24
Median $114,899 $11,366 $11,934 $2,500 $2,500
Weighted Mean $127,031 $16,015 $17,094 $5,177 $14,177
Weighted SE 4329 600 3264 388 2228
Some College (n=2221) Prop. holding debt 0.25 0.39 0.27 0.31 0.25
Median $140,224 $12,287 $10,974 $2,900 $1,900
Weighted Mean $149,206 $16,863 $19,028 $4,847 $9,283
Weighted SE 4009 570 824 278 1444
Associate Degree
(n=596)		 Prop. holding debt 0.33 0.51 0.45 0.40 0.26
Median $130,705 $14,022 $18,337 $3,000 $1,225
Weighted Mean $138,660 $16,891 $22,440 $5,478 $6,782
Weighted SE 5861 818 1179 469 1841
Bachelor's Degree
(n=1487)		 Prop. holding debt 0.44 0.48 0.51 0.36 0.14
Median $170,265 $15,242 $21,949 $4,000 $1,300
Weighted Mean $189,913 $17,136 $29,673 $6,763 $8,006
Weighted SE 3958 521 1194 352 2791
Advanced Degree (n=543) Prop. holding debt 0.47 0.42 0.50 0.29 0.09
Median $197,540 $16,414 $53,932 $5,000 $1,600
Weighted Mean $215,747 $17,278 $66,686 $6,158 $12,839
Weighted SE 6754 821 3843 412 6298
Race/Ethnicity White
(n=3611)		 Prop. holding debt 0.36 0.43 0.27 0.32 0.22
Median $148,155 $13,169 $19,585 $4,000 $2,000
Weighted Mean $165,652 $16,987 $31,467 $5,874 $11,258
Weighted SE 2549 349 1261 212 1327
Black
(n=2032)		 Prop. holding debt 0.10 0.24 0.31 0.17 0.18
Median $120,170 $10,974 $21,949 $2,000 $1,750
Weighted Mean $143,135 $14,568 $30,809 $4,127 $6,502
Weighted SE 8020 691 1215 354 1013
Hispanic (n=1632) Prop. holding debt 0.20 0.35 0.22 0.28 0.17
Median $152,417 $13,639 $16,462 $3,000 $1,750
Weighted Mean $167,680 $17,051 $29,043 $5,621 $6,456
Weighted SE 5940 625 2339 387 1354
Asian/Pacific Islander
(n=120)		 Prop. holding debt 0.31 0.35 0.28 0.25 0.10
Median $210,709 $15,577 $21,949 $5,000 $5,500
Weighted Mean $232,012 $19,269 $36,875 $6,648 $23,366
Weighted SE 18276 2764 6455 1150 16327
Native American (n=36) Prop. holding debt 0.08 0.42 0.41 0.49 0.32
Median $178,211 $12,944 $16,180 $2,500 $10,000
Weighted Mean $176,986 $15,701 $27,053 $2,892 $48,000
Weighted SE 23364 2984 5049 483 34315
Other or Multiple Races
(n=110)		 Prop. holding debt 0.25 0.30 0.31 0.31 0.15
Median $213,572 $15,242 $26,540 $4,250 $1,875
Weighted Mean $211,022 $16,251 $39,458 $5,059 $28,183
Weighted SE 19644 1726 6356 827 22587
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Data Analysis Strategy

We use logistic regression models to estimate the association between highest education, race, and debt holding. We use Cragg’s (1971) tobit alternative model to examine patterns in the amount of debt held. Cragg’s tobit alternative model, sometimes called a double hurdle model, combines a probit model with a truncated regression model. We use the exponential form of the double hurdle model to accommodate the distribution of the dependent variable. The tobit alternative model allows the parameters to vary both for predicting the probability of debt holding and for predicting the amount of debt held. Thus, we can use these models to generate the predicted value of debt conditional on the likelihood of holding a particular debt type.

To aid interpretation, we focus on predicted probabilities and values of debt holding generated from our models with all covariates set to their means with full models reported in Appendix A.4 Predicted probabilities report the differences between groups in the metric of the dependent variable for all possible comparisons between groups (Mize 2019). We include the no college group in the analysis of student debt holding to retain the same sample across models and because we consider all emerging adults to be at risk of holding student loans, given that the decision to enroll in higher education involves evaluating whether to take on debt.

We estimate an interaction between race/ethnicity and education to assess whether any association between education and debt holding varies across racial or ethnic groups. To assess the significance of the interaction, we estimate predicted probabilities of debt holding and use second difference tests for statistical significance (Mize 2019). These second difference tests compare differences in the predicted probabilities within one category of the interaction term across categories of the other interaction term. We repeat this approach by examining the amount of debt held using Cragg’s double hurdle models, including interactions at both tiers of the analysis. We rely on the second difference tests as the most precise estimates of whether the association between education and debt-holding varies by race/ethnicity.

Results

Our findings demonstrate Millennial emerging adults accrued significant debt, but in strikingly unequal patterns. The patterns by education, race/ethnicity, and education-by-race/ethnicity support our expectations of a combination of linear and nonlinear associations of socioeconomic advantage with debts differentiated by unequal access and use.

Unequal Debts by Education at the End of Emerging Adulthood

We find nonlinearities in the association between debt and education that is consistent with our theory of the combination of access and need in generating unequal debts. Figure 1 presents the predicted probability of debt holding (top panel) and the predicted value of debt (bottom panel) for the association between education and debt. Figure 2 quantifies the predicted probabilities for each group and tests for significant differences between groups. (See Appendix A for tables with the full models.)

Figure 1. Predicted Debt Holding and Debt Values by Education at the End of Emerging Adulthood.

Figure 1.

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Note: Panel A shows the predicted probability of debt holding at the end of emerging adulthood, derived from logistic regression models. Panel B shows the predicted value of debt holding among those who have debt, generated from the Cragg’s tobit alternative double hurdle model. Predicted probabilities and values are calculated from the models shown in Appendix Tables A1 and A2, respectively, with controls for degree, children, marital status, age, employment history, region, and whether the respondent was living in an urban area. Probabilities are generated using the margins command in Stata 15, with all covariates set to their means.

Figure 2. Predicted Probability of Debt Holding and Predicted Value of Debt Holding Comparisons by Education.

Figure 2.

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Note: This graphic reports tests of significant differences in the predicted probabilities of debt holding and debt values reported in Figure 1. The top panel of Figure 1 corresponds with the left panel of Figure 2, and the bottom panel of Figure 1. For debt type, the predicted amount is in bold along the main diagonal. Predicted values are generated from Cragg’s tobit alternative models of debt holding (Appendix Table A2). Probabilities generated using the margins command in Stata 15, with all covariates set to their means. Differences in predicted probabilities are shown off of the main diagonal, calculated using the mlincom command in Stata 15. Shaded cells indicate significant differences between groups (p<.05). Cells shaded green indicate the more educated group with is more likely to hold that debt type, while cells shaded in red indicate the more educated group is less likely to hold that type of debt. Non-significant differences are unshaded.

Figure 1 shows that housing debt (1) follows the most linear patterns as expected for this debt used for investment in property with the most restrictive credit access. Housing debt shows a positive linear association that is close to monotonic with the no college group least likely to hold housing debt, with increasing likelihoods for the groups with more education up to the advanced group most likely to hold housing debt. Figure 2 shows that all group differences are significant, except for the difference between bachelor’s and advanced degree holders.

The other four categories of debt follow more nonlinear patterns in the likelihood of debt holding by educational groups. The highest likelihood of holding car debt (2) falls in the middle of the educational groups. The associate and bachelor’s degree groups had the highest predicted probabilities of holding car debt (.44 and .45, respectively). Advanced degree holders were less likely to hold car debt than associate and bachelor’s degree holders, about the same as those with some college, and more likely than those without college experience. The no college group was significantly less likely to hold car debt than all other groups, just as for house debt. Advanced degree holders and those with some college had similar likelihoods of holding car debt.

The likelihood of holding student debt (3) also diverges from a straightforwardly linear pattern. All three of the most educated groups had similarly high likelihoods of carrying student debt. The bachelor’s degree group was most likely to hold student debt, albeit comparable to the advanced degree group. The associate group was less likely to hold student debt than the bachelor’s group. The some college group had a significantly lower likelihood than the other three groups with degrees. Strikingly, even though those with an associate degree accumulated far less educational capital than those with advanced degrees by age thirty, they had about the same likelihood of carrying student debt at the end of emerging adulthood.

For credit card debt (4), the peak again falls in the middle of the distribution of educational groups with relatively similar likelihoods among all groups with some college enrollment. Associate degree holders were most likely to carry credit card debt, with a predicted probability of .34 when all covariates are set to their means. The pattern echoes that of car debt, where the significant differences concentrate among those with the least and the most education. Strikingly, advanced degree holders were significantly less like to hold credit card debt than those with associate or bachelor’s degrees. The no college group stands out by holding significantly lower levels of credit card debt, likely partly because of lower credit access.

The other debt to businesses (5) to some degree shows a negative linear association as expected, with the advanced degree group the least likely to hold this debt. This category is also the only form of debt that no college and some college groups are more likely to carry than the advanced degree group. Yet the association also shows some nonlinearity: Other debts to businesses follow the inverse of college debt in being weighted at the bottom rather than the top of the distribution of educational groups. The associate degree, some college, and no college groups all had similarly high probabilities of carrying other debts to businesses, showing significant economic vulnerability even among emerging adults who are striving towards greater educational attainment.

The inequalities in debt values (reported in the bottom panel of Figure 1 and right panel of Figure 2) followed similar patterns to inequalities in the likelihood of holding a debt, but there are several instructive differences that further develop our understanding of unequal debts. First, when those with advanced degrees hold student debt, they hold significantly higher amounts relative to the associated and bachelor’s degree groups. Second, the pattern in levels of credit card debt differed somewhat from the probability of holding a credit card balance. Emerging adults with advanced degrees were less likely to carry credit card debt, but when they did carry that debt, they carry similar levels to those with bachelor’s and associate degrees. Third, we find even more inequality in total other business debt than in the likelihood of holding that diverse category of debt, where those with the least education hold the highest amounts of debt. The high levels of other business debt among the non-college and some college groups stand out when comparing the very low levels of debt owed in every other debt type for that group.

Unequal Debts by Race/ Ethnicity at the End of Emerging Adulthood

We find unequal debt-holding by race/ethnicity consistent with our expectation of systematic credit market exclusion for Black emerging adults. We also find evidence of lower credit access for Hispanic relative to White emerging adults, but as expected the Hispanic group appears in an intermediate position relative to the Black and White groups. The top panel of Figure 3 shows the predicted probabilities of holding each type of debt by race/ethnicity and the bottom panel shows the predicted value of debt for debt-holders (again derived from the models presented in Appendix Table A1 with all covariates set to their means). Tests for significant differences in the amount of debt held echo the pattern in the figure exactly and so we report those in Appendix Figure A1.

Figure 3. Predicted Probability of Debt Holding by Race/Ethnicity Around Age 30.

Figure 3.

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Note: Panel A shows the predicted probability of debt holding around age 30, derived from logistic regression models. Panel B shows the predicted value of debt holding among those who have debt, generated from the Cragg’s tobit alternative double hurdle model. Predicted probabilities and values are calculated from the models shown in Appendix Tables A1 and A2, respectively, with controls for degree, children, marital status, age, employment history, region, and whether the respondent was living in an urban area. Probabilities are generated using the margins command in Stata 15, with all covariates set to their means.

Black and Hispanic emerging adults were less likely to hold all types of market debt—but more likely to hold student debt –than White young adults and net of the other covariates. Compared to Black young adults, Hispanic young adults were significantly more likely to hold house, car, and credit card debt. However, Hispanic young adults were significantly less likely to hold student debt than Black young adults. There were no significant differences between Black and Hispanic young adults in the likelihood of carrying the more open access other business debts. The pattern of results by race for total value of debt held (bottom panel) follows similar patterns for the likelihood of debt holding. Black young adults stand out as having the lowest levels of debt holding for house, car, and credit card debt. Hispanic young adults carry similar levels of car debt and student loan debt to White young adults, and similar levels of other business debts to Black emerging adults. Even more in the case of racial inequalities compared to educational inequalities, debt-holding and debt levels are driven by similar exclusion factors.

Unequal Debts by Education-by-Race/Ethnicity

We find that unequal debts by education vary by race, and in patterns consistent with our expectations of substantial racial inequality in access to and need for credit even for highly educated Black and Hispanic emerging adults. We find particularly strong evidence of unequal debts for Black relative to White emerging adults. Hispanic emerging adults again appear in an intermediate position, consistent with the diversity of this population. Figure 4 reports predicted probabilities of debt holding in the top panel and debt values in the bottom panel (models in Appendix A). And Figure 5 tests the relationships reported in Figure 4 by quantifying the differences in the predicted probabilities within and between racial and ethnic groups with a chi-square test for significant differences in the heights of the bars (Mize 2019). For each panel of Figure 5, the first row (labeled with the educational comparison) compares the predicted probabilities within each race/ethnic group that are displayed in Figure 4. The second row (labeled “second difference”) compares the differences in predicted probabilities for Black and Hispanic emerging adults relative to the difference for White emerging adults. (Recall that here we measure education in three categories—no college degree, associate degree, and bachelor’s or more—to maintain sufficiently large sample sizes when interacting with race/ethnicity).

Figure 4. Predicted Probability of Debt Holding and Debt Values by Education and Race/Ethnicity.

Figure 4.

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Note: The top panel shows the predicted probability of debt holding at the end of emerging adulthood, derived from logistic regression models (Appendix Table A3). The bottom panel shows the predicted value of debt holding among those who have debt, generated from the Cragg’s tobit alternative double hurdle model (Appendix Table A4).Note that “No Degree” means no college degree; most in this category hold a high school diploma or equivalent.

Figure 5. Differences in the Predicted Probability of Debt Holding by Education, with Second Difference Tests across Race.

Figure 5.

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Note: This graphic reports tests of the differences in the predicted probabilities of debt holding and debt values, as reported in Figure 3, generated from the models in Appendix Tables A3 and A4. We show only the second difference results for the Black-White and Hispanic-White comparisons as those are of particular theoretical interest. Cells shaded light green (lightest of light grey in greyscale) indicate the group with more education is more likely to hold that type of debt, cells shaded light red (darker grey) indicates the group with more education is less likely to hold that type of debt. Dark green (dark grey and positive) indicates a significant positive second difference Chi Square test (Mize 2019), where more education is associated with a higher probability of holding a particular type of debt for White emerging adults relative to Black or Hispanic emerging adults. Dark red (dark grey and negative) indicates a significant negative second difference Chi Square test (Mize 2019), where more education is associated with a lower probability of holding a particular type of debt for White emerging adults relative to Black or Hispanic emerging adults.* p<.05, ** p<.01, *** p<.001

For housing debt holding (1), in the top panel of Figure 4 and left panel of Figure 5, the association between education and debt holding is similar across race/ethnic groups. As expected, education raises use of mortgage credit for all race/ethnic groups, but there is no racial difference in the degree to which education matters. Racial differences concentrate in the main effect of race/ethnicity discussed in the last section, whereby Black and Hispanic emerging adults are less likely to carry mortgages than White emerging adults. A chi-square test confirmed the interaction term improved overall model fit, but none of the second differences in predicted probabilities in Figure 5 are statistically significant.

The probability of holding car debt (2) by education demonstrates that education provides some advantage in gaining access to credit for educated Black and Hispanic emerging adults, but also higher indebtedness relative to educated White emerging adults. The differences between associate degree holders and those without a college degree were similar across race/ethnicity. But the association between car debt and having a bachelor’s degree relative to an associate degree was positive for Black youth, negative for White youth, and null for Hispanic youth. The second difference tests indicate that the White-Black difference is statistically significant. While a bachelor’s degree is associated with a higher likelihood of debt holding across race/ethnicity, the association between education and debt holding is significantly stronger for Black and Hispanic young adults relative to White young adults.

The overall pattern of student debt holding (3) is similar across race/ethnicity, but there are significant racial differences in the size of gaps in likelihoods. While a bachelor’s degree is associated with an increased probability of debt holding for all race/ethnic groups, the difference relative to those with no college degree is significantly larger for Black than White emerging adults. Black and Hispanic youth who achieved education were much more likely to have to take on student debt than White emerging adults. The differences between bachelor’s and associate degree holding were insignificant across racial groups for student debt indicating that despite achieving a lower degree, associate degree holders are similarly likely to take on student debt.

For higher-cost unsecured credit card debt (4), there were large differences in the association between education and debt holding by race. Looking only at White young adults, we see the same nonlinear pattern evidenced in Figure 1, where associate degree holders are significantly more likely to hold credit card debt than Black or Hispanic young adults. In contrast, for Black emerging adults, the relationship appears linear, where bachelor’s degree holders were significantly more likely to hold credit card debts. Among Hispanic young adults, both bachelor’s and associate degree holders are significantly more likely than those with no degree to hold credit card debt, though they do not differ significantly from each other. Looking across race, the differences between the bachelor’s group and both the associate degree group and the no college group differs between Black, White, and Hispanic young adults. Increased education exposes Black and Hispanic young adults to credit card debt, while White young adults with a bachelor’s degree seem to experience some degree of protection from these higher cost debts.

For the riskiest other debts to business (5), there are once again stark differences by race. For both White and Hispanic emerging adults, having a bachelor’s degree relative to an associate or no college degree is associated with a significantly lower probability of debt holding. The protective effect of a bachelor’s degree on the likelihood of holding these other debts to businesses is significantly stronger for White young adults relative to both Hispanic and Black young adults.5 In striking contrast, in this case, there is no significant association between education and debt holding for Black young adults meaning that even highly educated Black emerging adults are most exposed to the highest cost debts.

The bottom panel of Figure 4 reports the moderating effect of race on the predicted value of debt held and the right panel of Figure 5 reports the second difference tests of the predicted dollar values of debt holding for each comparison. We present these as more exploratory as Tier 2 of these models relies on the much smaller sample of those carrying debt within each group. The results for debt values mostly reflect the same patterns as the results for debt holding. For example, White emerging adults carry larger mortgages in addition to having a greater likelihood of taking on home mortgages.

Discussion

We find that emerging adults accumulate significantly unequal debts by the end of emerging adulthood. The pattern of results supports our theoretical expectations of a combination of linear and nonlinear associations for educational attainment, race/ethnicity, and the interaction of education and race. Main effects by education demonstrate that the most educated were most likely to carry debts that enable investment and social mobility and least likely to carry debts incurred for consumption and emergencies. The least educated showed just the opposite pattern. However, that linear association captures only part of the pattern of unequal debts. Consistent with inequalities in access to and need for distinct debt types, the middling educational groups—associate degree and some college—experienced high exposure to debt. Strikingly, the associate degree group was particularly likely to carry a range of debts, with a similar probability of carrying car loans and credit card debt as the bachelor’s degree group, and the same likelihood of carrying the riskiest other unsecured debts as the no college and some college groups. Our findings thus reveal the financial fragility underlying attainment at the lower tier of higher education for the Millennial cohort.

Main effects by race/ethnicity support our expectations that Black and to a lesser extent Hispanic emerging adults in the Millennial continue to have significantly lower access to credit markets. Our findings support the growing consensus that differential credit access is an important element of the Black-White wealth gap (Killewald 2013; Conwell and Ye 2021). We find that Black and Hispanic emerging adults accrue fewer financial market debts across the board. However, Black emerging adults carry higher student loan debt, which is mainly distributed through the federal student loan system, relative to White and Hispanic emerging adults. By analyzing student loans in the comparative context of the other common types of youth debt, our findings suggest that lack of access to other forms of credit may be one of the mechanisms driving higher student loan burdens among Black populations (Addo et al. 2016). Our findings highlight the racialized inequalities that go well beyond inequalities in student debt and point to racial disparities in credit access more broadly, including especially low access among Black emerging adults, as well as reinforcing systemic racism as a defining feature of American life (Bonilla-Silva 1997, 2021; Conwell and Ye 2021).

Finally, in our analysis of the interaction of education and race/ethnicity, we find significant moderation of the association between education and unequal debts by race/ethnicity for the Millennial cohort. Our finding that associate degree holders were particularly likely to carry many types of debt concentrates mainly among White emerging adults. In contrast, the most educated Black and Hispanic emerging adults were more vulnerable to the highest-cost debts than were the most educated White emerging adults. Indeed, the most educated Black group carry debts in a pattern similar to White emerging adults with an associate degree. Hispanic emerging adults with higher education appear to attain some, but not all, of the protective effects of a bachelor’s degree compared to White emerging adults. Finally, Black emerging adults without a college degree held little debt except for the highest-cost debts, consistent with our expectation of racial barriers to credit access. Hispanic youth without a college degree also had lower car and credit card debt than White youth. The findings are consistent with our expectations that contemporary and historical racism structure distinct credit access for Black, White, and Hispanic emerging adults. Distinctively high levels of student debt holding among Black youth demonstrate significant investment in higher education among this population. In the context of the full range of debt holding, high student loan use may also occur partly because of low credit access in other forms of debt. Black emerging adults who earned a bachelor’s degree were exposed to the riskiest types of debt despite educational achievements. In addition to fueling racial inequality, these dynamics may result from labor market discrimination and exclusion with rebounded impacts on social mobility.

Conclusion

We have mapped a landscape that we hope will inform future research, including inquiry into the causal mechanisms that produce that terrain. Our findings demonstrate the critical role of credit and debt in emerging adulthood, consistent with research on the importance of family wealth and the valuation of White lives over Black, Hispanic, and other non-White lives within the financialized context of the transition to adulthood in America (Bonilla-Silva 2006; Addo et al. 2016; Rucks-Ahidiana 2022; Norris 2023). Yet our findings also likely understate unequal debt holding. The NLSY97, like many social surveys, lacks measures of interest rates and other terms which differentiate debts within each category. Moreover, the types of debt most likely held by disadvantaged populations such as payday loans and past due bills get collected in the “other debts to businesses” variable, but many debts such as legal debt or child support debt may get underreported in such a general question. Better data on the timing of the taking on and paying back of debt would enable disentangling these various sources of unequal debts, particularly in understanding debt trajectories across the young adult life course. Even with limitations, the NLSY97 provides sufficient detail to reveal inequalities for emerging adults coming of age in the 2000s and indicate important directions for future studies.

Our work also supports a growing concern with the development of financial identity and financial well-being as core elements of the hallmarks of emerging adulthood. Prior research has identified the importance of family support and socialization in developing financial identity and well-being (Lanz and Serido 2020; Burcher et al. 2021; Vosylis and Klimstra 2022; Vosylis et al. 2022). One of the many interesting directions could be to further examine the degree to which family financial socialization considers debt instruments and the balance between investment, attainment, consumption, and emergency forms of debt. Unequal debts have implications for the development of financial identity and well-being and also financial socialization and development may be core sources of the unequal debts we have identified (Rucks-Ahidiana 2022). Stress about finances may reduce the capacity of young adults to engage in identity exploration and may limit their sense of the possibilities for the future (Silva 2016; Cherney et al. 2020). Debt holding may link to other problem behaviors linked to inequality in emerging adulthood (Hays et al. 2021). At the same time, financial access and pressures may be a significant influence on both the subjective and objective features of all five dimensions of emerging adulthood, including identity explorations, instability, self-focus and in between-ness and possibilities/optimism. Emerging adults must grapple with choices about whether to take on debt in the face of an uncertain future, and many may have limited resources to evaluate those decisions (Cherney et al. 2020). Nelson (2021) identifies burdensome indebtedness as related to the range of experiences of flourishing and floundering through the second decade of life that results in differential readiness for a rewarding third decade.

We close with a call for greater support for the public goods that would enable more secure emerging adulthoods and greater equality and inclusion across emerging adults. Our findings of unequal debts highlight the damaging result of disinvestment in higher education as well as other financial insecurities in American life that fell particularly hard on the Millennial cohort as the financial costs of emerging adulthood were shifted to individual emerging adults and their families (Houle 2014; Cherney et al. 2020). Yet society at large benefits from healthy development and secure well-being as young people make the transition to adulthood. Society also benefits from reducing inequalities so that the full human potential of our populations can be developed. Great subsidy of higher education, expansion of Pell Grants and reduced reliance on unrealistic expectations about parental financial contributions would enable more emerging adults to explore their options without carrying debt into the third decade of life. But the disproportionate reliance of Black and Hispanic emerging adults on student loans also suggests there may be potential unintended consequences of reducing access to student loans without increasing access to resources in other ways (Addo et al. 2016; Norris 2023).

Proposals for basic income grants or baby bonds would provide an alternative to the private market for individuals and families to support their emerging adulthood (Hamilton and Darity 2017). With or without such investments, creating fair and open access financial markets is urgently needed. Regulating the highest costs loans is one key policy response that has been successful in at least some places in reducing burdensome debt traps. Encouraging the growth of more nonprofit financial organizations would potentially support much more open access, including credit unions and investing in postal banking (Wherry et al. 2019; Block and Hockett 2022). While such changes would require significant state investment, the Millennial and later cohorts who have faced such significant financial burdens during emerging adulthood may provide the political will to support such change, arguably through similar mechanisms as for growing support for unionization (Tapia and Turner 2018). These proposals vary in political feasibility. Yet as the unprecedented government support during the COVID-19 pandemic illustrates, political opportunity can change quickly (Parolin 2023). Thus, it is valuable to develop blueprints that can be followed in times of greater opportunity. As the Millennial cohort ages, the consequences of unequal debts will continue to have significant implications for their entire families. Whether unequal debts grow or subside over the life course for this Millennial cohort will be a crucial indicator and consequence of our path forward.

Supplementary Material

1

Acknowledgements:

Support for this project was provided by a research grant from the National Endowment for Financial Education (NEFE) and by the Ohio State University Institute for Population Research through a grant from the Eunice Kennedy Shriver National Institute for Child Health and Human Development of the National Institutes of Health, P2CHD058484.

Biographies

Rachel E. Dwyer is Professor of Sociology and Faculty Affiliate of the Institute for Population Research at The Ohio State University. She studies the causes and consequences of rising inequality in the United States, with a particular focus on credit, debt, and inequality in emerging adulthood. Her studies of emerging adults have appeared in Social Science Research, Social Forces, and Socio-Economic Review among other outlets.

Laura M. DeMarco is Assistant Professor of Sociology at North Carolina State University. Her research focuses on various forms of inequality reflected in and created by the criminal justice system, including inequalities in credit and debt. Her work focuses on emerging adults and navigating the criminal justice and educational institutions during adolescence and emerging adulthood and has appeared in Criminology and the Handbook of the Criminology of Terrorism.

Footnotes

1

We confirmed the three-category variable follows the general pattern of results as the five-category variable. We also tested the five-category variable with the racial interaction and the same pattern holds. Results available upon request.

2

The sample is too small to separately analyze racial groups within the Hispanic population.

3

Table 2 shows summary measures for Asian/Pacific Islander, Native American, and Other Multiple Races groups with small samples. We see patterns for these groups consistent with patterns of wealth inequality in prior studies (Nembhard and Chiteji 2006). The Asian/Pacific Islander group was the second most likely to carry mortgages after White emerging adults and even less likely than the White group to carry unsecured debts. Among debt holders, Asian/Pacific Islanders carried higher levels of debt than White populations. This pattern is consistent with substantial variability in the socioeconomic position of Asian/Pacific Islander groups. Native American emerging adults report low levels of mortgage debt holding and balances and high levels of unsecured debts. Native American youth held very high levels of car debt, perhaps reflecting higher than average residence in rural areas. While a tiny sample (n=36), this highly disadvantaged pattern is consistent with other research on persistent disadvantage among a population facing systematic racism, dispossession, and genocide throughout US history. The other and multiple races group follows a pattern like the Hispanic group, perhaps because Hispanic ethnicity populations also identify with a diverse set of races. Our findings for the three smaller groups are tentative but serve as a reminder of the broader unequal racial landscape that we cannot model in our multivariate analyses.

4

Baseline models with only our main predictors of education and race/ethnicity show quite similar results to the full models (results available upon request).

5

We examine whether findings are robust to two key additional controls. First, we control for household income at age thirty and find a nearly identical pattern of results. The robustness of our results is consistent with expectations that financial challenges in emerging adulthood go beyond the income resources available at a given time. Second, we control for whether the respondent ever attended a for-profit college, which may significantly drive unequal debt holding because these high-cost, but low-return schools disproportionately enroll low-income students and students of color (McMillan Cottom 2017). We find that attending a for-profit institution was associated with a riskier debt portfolio, but the associations between education and debts including by race/ethnicity remained.

Contributor Information

Rachel E. Dwyer, The Ohio State University.

Laura M. DeMarco, North Carolina State University

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