What a bachelor’s degree is worth today: a comparative survey of income, career advancement, and demographic disparities

This study examines variation in job prospects, earnings, and perceived career alignment across educational attainment and discipline, and evaluates how academic major intersects with race and gender to shape the financial payoffs of a bachelor’s degree. Drawing on survey data from 2,698 U.S. adults (61.7% graduates, 38.3% non-graduates), multiple regression and ANOVA analyses revealed that graduates earn substantially more and are more likely to be employed full-time than non-graduates. Among degree holders, STEM and business majors reported the highest earnings, and humanities and social sciences majors reported lower salaries alongside higher job relevance and stronger career satisfaction scores. Black and Hispanic graduates, and women across majors, earned significantly less than White men holding the same degrees, and the study did not find interaction effects between identity and field of study. These findings confirm that a bachelor’s degree supports upward mobility, and salary gaps by race and gender persist even among participants grouped by major. The results affirm that the economic value of a bachelor’s degree is unevenly distributed across social groups. Policy implications include restructuring federal and institutional aid programs, including the FAFSA system and need-based aid design. The findings are interpreted as descriptive patterns observed within a large cross-sectional survey, without causal or mechanistic claims about labor-market inequality.

1 Introduction

Despite widespread belief in the economic advantages of a college degree, its return on investment varies significantly across race, gender, and fields of study (i.e., academic majors). As labor market studies show, college graduates earn more and enjoy greater job stability than non-graduates, yet these aggregate advantages tend to conceal disparities related to field of study and demographic background (Ghosh, 2014). Such disparities raise important questions about who really benefits from higher education—and under what conditions (Webber, 2014). To understand the unequal distribution of the economic returns of a college education, this study draws upon human capital theory (HCT), which says that a bachelor’s degree should lead to higher earnings and greater job security (Oreopoulos and Petronijevic, 2013). HCT argues that investments in a college education should yield proportional returns on the labor market, yet the empirical evidence implies that the magnitude and consistency of such returns depend heavily on contextual factors—most notably, field of study, gender, and race (Alon and Tienda, 2005; Arcidiacono, 2004; Webber, 2014).

Among the three main factors that shape long-term economic returns to higher education, academic major exerts the most significant influence. Collegians who earn degrees in STEM or business consistently report higher earnings than those who major in the humanities, the social sciences, or the arts. This earnings advantage has been documented: Webber (2014) identified substantial lifetime income differences across majors, and Arcidiacono (2004) found that while ability sorting accounts for part of these variations, it does not explain it entirely. These findings suggest that even though field of study plays a major role in determining economic outcomes, its impact is often mediated or compounded by other contextual variables, such as personal aptitude, prevailing labor market conditions, and institutional prestige. Moreover, monetary returns alone cannot capture the broad value of a bachelor’s degree. Graduates from lower-paying majors often report greater job satisfaction and a stronger alignment between their work and personal values, highlighting the multidimensional nature of educational returns (Oreopoulos and Salvanes, 2011).

Gender-based disparities add an additional layer of complexity to the analysis of postsecondary outcomes. Although women now surpass men in college completion rates, they continue to earn significantly less across nearly all occupational sectors (Sloane et al., 2021). According to Alon and Tienda (2005), women get lower financial returns on their degrees even when employed in comparable roles and majors. Blau and Kahn (2017) attribute this wage gap to several structural barriers, including occupational segregation and restricted opportunities for advancement. While the gender pay gap is narrower in fields such as education and healthcare, it widens considerably in male-dominated professions such as engineering and accounting (Blau and Kahn, 2017). These disparities illustrate how gender intersects with labor market structures and academic choices to determine post-graduation economic outcomes.

Along similar lines, research has suggested that racial and ethnic inequities also play a role in shaping the postsecondary economic landscape. While college graduation has gone up among historically underrepresented groups, the financial benefits of higher education remain unevenly distributed. Black and Hispanic graduates, even with comparable credentials and roles, earn less than their White peers (Chetty et al., 2020). These gaps cannot be entirely attributed to personal decisions or academic choices; rather, they reflect a systemic inequality embedded in the education system and the labor market. This broader disadvantage is further compounded by institutional and financial barriers that disproportionately burden students of color. As Houle and Addo (2019) have observed, students from marginalized racial and ethnic groups are more likely to enroll in underfunded institutions and end up with heavier student debt liabilities. To be sure, these disadvantages do not work in isolation: labor market discrimination, structural racism, and unequal access to academic resources intersect with them to impede economic mobility—even for those with similar educational credentials—and reinforce the stratified outcomes of higher education.

To explore how these disparities manifest in real-world outcomes, this study relied on data from a stratified sample of college graduates and non-graduates. Using a cross-sectional survey design, it captured differences across fields of study, racial and gender identities, and socioeconomic backgrounds. Prior research has shown that access to selective institutions and labor market returns varies by race, gender, and class, often reproducing rather than mitigating existing inequalities (Perna, 2005). By jointly examining earnings, employment, and subjective career outcomes across graduates and non-graduates within a single survey framework, this study provides descriptive confirmation of well-documented labor-market patterns while extending prior work through the inclusion of non-monetary outcomes and early-career respondents.

2 Empirical background

Over the past several decades, the bachelor’s degree has been widely viewed as a reliable lever for upward economic mobility and job stability. Numerous empirical studies have reported a persistent wage premium associated with earning a bachelor’s degree, reinforcing the view of college as a rational investment in human capital (Carnevale et al., 2011; Tamborini et al., 2015).

Despite short-term labor market disruptions and broader macroeconomic volatility, the long-run earnings advantage conferred by a four-year degree has remained remarkably stable (Belfield and Bailey, 2017). These trends are frequently explained through the lens of Human Capital Theory (HCT), which asserts that education enhances people’s productivity and skills, leading to higher labor market returns on investment (Becker, 2008; Marginson, 2019; Oreopoulos and Petronijevic, 2013). HCT treats academic attainment, such as a bachelor’s degree, as a deliberate investment whereby people allocate resources (e.g., time and tuition) in expectation of future income gains.

Building on this theoretical and empirical foundation, a substantial body of research has reinforced the broad validity of HCT at the population level. College graduates, on average, earn more over their lifetime, have lower rates of unemployment and experience higher occupational stability than non-graduates (Kim et al., 2015; Tamborini et al., 2015). These patterns have been observed in different contexts and occupational sectors, and have informed several public policy frameworks designed to promote completion as mechanisms of economic security (Carnevale et al., 2011). Although these aggregate outcomes support HCT’s general assumptions, they tend to mask major sources of variations in post-college returns. Differences in field of study, race, and gender exert significant influence on the labor market experiences of graduates and pinpoint the limitations in the explanatory reach of standard economic models (Alon and Tienda, 2005; Perna, 2005).

But a number of education scholars have questioned the adequacy of HCT in accounting for systemic inequities in postsecondary outcomes. For example, Brand and Xie (2010) confirm that educational returns are not evenly distributed between demographic groups, in part because of racialized labor markets, occupational segmentation, and institutional stratification. Research also demonstrates how unequal access to advising, financial resources, and social capital shapes the capacity of students from marginalized communities to convert educational attainment into economic mobility (Brighouse et al., 2018; Perna, 2005). These research findings challenge the assumption that similar educational inputs produce similar market outcomes, especially for first-generation students, students of color, and women in male-dominated sectors. One of the visible manifestations of these unequal returns lies in the economic disparities linked to academic major.

Many empirical studies show that students who major in business or STEM command higher earnings and early-career outcomes that outstrip those of their peers in the humanities, education, and social sciences (Altonji et al., 2012; Arcidiacono, 2004; Webber, 2014). These disparities persist even after controlling for academic performance and institutional selectivity, which implies that field of study plays an independent role in shaping post-college trajectories (Altonji et al., 2016). Indeed, for students from low-income or underrepresented backgrounds, major choice is often constrained by financial pressure, resulting in strategic selection of higher-paying majors to offset the risks associated with student debt (Goldrick-Rab, 2010; Rothstein and Rouse, 2011). Otherwise stated, field-based differences not only influence economic returns but also interact with structural inequality to shape educational decision-making.

Yet even among collegians who pursue lucrative majors, economic outcomes seem to remain stratified along racial and gender lines. Research by Chetty et al. (2020) clearly shows that Black and Hispanic college graduates earn significantly less than their White peers, even when degree and industry types are experimentally controlled. Houle and Addo (2019) attribute this earnings gap in part to disproportionate debt burdens and differential access to high-return institutions. Similarly, gender disparities remain entrenched across virtually all academic fields, including those in which women are numerically overrepresented (England, 2010; Perna, 2005).

Xu (2017) found that women in STEM face persistent barriers to advancement, such as exclusion from mentorship networks and scant promotion opportunities. Viewed as a whole, these findings suggest that race and gender continue to shape access to educational pathways and the economic returns that those academic choices produce. This expanding body of research has clarified many sources of differential returns to higher education; however, several areas remain underrepresented within single-dataset analyses.

Three recurring limitations characterize much of the existing empirical literature on postsecondary returns. First, many studies rely on large-scale administrative or longitudinal datasets that prioritize market outcomes such as income and employment, with less consistent attention to subjective dimensions of post-college experience. Indicators such as job satisfaction, perceived advancement, and occupational fit are included less frequently, despite their relevance for evaluating educational outcomes beyond earnings. Second, a substantial portion of the literature focuses exclusively on degree completers, often excluding individuals who attended college without graduating and thereby narrowing the scope of comparison (National Student Clearinghouse Research Center, 2024). Finally, many analyses rely on aggregate group comparisons that do not examine how demographic characteristics and academic fields co-occur within a single analytic framework, limiting descriptive insight into how race, gender, and field of study jointly relate to observed outcomes (Mulder et al., 2023).

This study responds to these coverage limitations by drawing on a large, cross-sectional dataset comprising 2,698 adults, including bachelor’s degree holders and non-graduates. Participants completed a comprehensive survey capturing employment and income information alongside subjective indicators such as job relevance, career satisfaction, and perceived barriers to advancement. By including graduates from a range of academic disciplines and demographic backgrounds, as well as individuals who attended college without completing a degree, the dataset supports a broader descriptive examination of postsecondary outcomes. The sample also includes a subset of early-career graduates (within 2 years of degree completion), enabling focused comparison of early labor-market experiences that receive less attention in long-term return-on-investment research (Cooper, 2021). The analyses presented in the following sections rely on t-tests, chi-square tests, analyses of variance, and multiple regression to describe how employment status, earnings, and job satisfaction vary by academic major and demographic group. Where relevant, interaction terms are included to assess whether observed outcome differences vary across demographic groupings, without asserting causal or mechanistic explanations. Taken together, these analyses provide a consolidated descriptive account of the economic and occupational value of a bachelor’s degree that integrates objective and subjective outcomes within a single analytic framework.

3 Methods

3.1 Research design

This study used a cross-sectional survey design to describe variation in economic and career outcomes associated with a bachelor’s degree. The analytic framework was informed by Human Capital Theory (HCT), which links educational attainment to labor-market productivity and earnings, but the study did not aim to test causal mechanisms or adjudicate among competing economic explanations. Instead, the analyses examined how observed outcomes differ across educational attainment, academic major, race, and gender within a single contemporary dataset. Data were collected using three independently administered surveys designed to capture wages, employment status, job satisfaction, and perceived job relevance across demographic and educational groupings. The study was explicitly descriptive in purpose and scope, focusing on patterns of association rather than causal inference.

3.2 Participants and sampling

Participants were recruited in 2025 through Prolific, a professional online survey platform commonly used for behavioral and social science research. A total of 2,856 U.S. adults (age 18 or older) elected to participate after viewing the study invitation through the Prolific interface and provided informed consent prior to participation. Respondents received modest financial compensation consistent with platform norms. After excluding 50 pilot participants, 38 cases lacking informed consent, and 70 incomplete or inconsistent responses, the final analytic sample consisted of 2,698 adults with complete data.

Sampling was stratified by gender, race or ethnicity, educational attainment, and geographic location to support balanced subgroup comparisons. The sample was not intended to generate nationally representative population estimates; rather, it was designed to support descriptive comparisons across educational and demographic categories. Of the final sample, 1,664 respondents (61.7%) held a bachelor’s degree and 1,034 (38.3%) had not completed a four-year degree. Among degree holders, 544 (32.7%) majored in business, 505 (30.3%) in STEM fields, 312 (18.8%) in the humanities, and 303 (18.2%) in the social sciences.

To facilitate examination of early-career outcomes, the dataset included a subset of 500 respondents who had completed their bachelor’s degree within 2 years of the survey administration. This subgroup consisted of the first 500 eligible respondents who met the early-career criterion and was selected to allow focused comparison rather than to represent all recent graduates. The full sample included 1,424 men (52.8%) and 1,274 women (47.2%). Racial and ethnic composition was as follows: 154 Asian (5.7%), 319 Black (11.8%), 467 Hispanic (17.3%), 1,649 White (61.1%), and 109 respondents (4.0%) identifying with other racial backgrounds. All responses were collected anonymously, and no identifying information was retained.

3.3 Measures and instruments

Data were collected using three online survey instruments tailored to graduates, non-graduates, and early-career respondents. Core variables included starting salary and current salary, employment status (full-time, part-time, unemployed), perceived job relevance, and career satisfaction. Job relevance was measured on a five-point Likert scale ranging from 1 (very low) to 5 (very high). Career satisfaction was assessed using a five-point Likert scale ranging from 1 (not satisfied at all) to 5 (very satisfied). All items were adapted from previously validated instruments used by the National Center for Education Statistics (National Center for Education Statistics, 2020) and the Pew Research Center (2016). Full survey instruments are provided in Appendices B–D.

A small pilot study was conducted to confirm item clarity and internal consistency prior to full data collection. Missing data were minimal (<2% across all variables). Listwise deletion was used to handle missingness in order to preserve transparency and comparability across analyses, given the limited extent of missing responses.

3.4 Data analysis

All analyses were conducted using RStudio version 4.2. The analytic strategy combined descriptive statistics with standard inferential techniques to characterize observed differences across educational and demographic groups. Independent-samples t-tests were used to compare graduates and non-graduates on starting salary, current salary, and perceived job relevance. Chi-square tests of independence assessed associations between employment status and categorical variables such as educational attainment and field of study.

Field-of-study differences among bachelor’s degree holders were examined using one-way analyses of variance (ANOVA) for starting and current salary. Where omnibus F-tests reached statistical significance, Tukey’s Honest Significant Difference (HSD) tests were used for pairwise comparisons. Two-way factorial ANOVAs were used to examine whether salary differences varied across combinations of field of study with race or gender. These analyses were intended to assess descriptive variation across groupings rather than to model intersectional mechanisms.

Multiple regression models were estimated to examine associations between current salary and field of study, race, and gender while statistically controlling for educational attainment and employment status. Interaction terms were included to assess whether observed salary differences varied across demographic categories, with results interpreted as descriptive moderation rather than evidence of causal interaction. Descriptive statistics, including counts and percentages, were also used to summarize job relevance ratings, career satisfaction, and reported barriers to advancement across educational, racial, and gender groups. All hypothesis tests were two-tailed with a significance threshold of α = 0.05. Effect sizes (Cohen’s d, partial η², and standardized β coefficients) and 95% confidence intervals were reported to aid interpretation of magnitude and practical relevance.

4 Results

4.1 Job opportunities, income, and career satisfaction by educational attainment

The analysis of the full sample (N = 2,698) revealed statistically significant differences in income and employment outcomes between college graduates and non-graduates. As shown in Table 1, college graduates earned an average starting salary of $59,773.44 (SD = 16,562.09), while non-graduates earned $34,625.11 (SD = 7,515.83)—a statistically significant difference, t(2696) = 45.97, p < 0.001.

Table 1

Table 1. Comparison of graduates’ and non-graduates’ starting salary, current salary, and job relevance ratings.

Similarly, graduates reported much higher current salaries (M = $84,556.53, SD = $18,513.92) compared to non-graduates (M = $59,782.66, SD = $10,988.96), t(2696) = 38.97, p < 0.001. Figure 1 shows the comparison between graduate and non-graduate starting and current salaries.

Figure 1

Figure 1. Boxplots comparing starting and current salaries between graduates and non-graduates.

In addition to earning higher starting and current salaries, college graduates were also more likely to be employed full-time. As shown in Table 2, 71.2% of graduates reported full-time employment, compared to 42.8% for non-graduates. A chi-square test of independence confirmed that such a difference was statistically significant, χ²(2) = 218.42, p < 0.001. These findings are congruent with prior research showing that higher education markedly improves employability and lifetime earnings (Baum et al., 2013; see also Kim et al., 2015). However, these economic advantages did not extend to all aspects of work life. Both graduates and non-graduates reported similar levels of perceived job relevance and career satisfaction, indicating that higher income and employment rates were not associated with higher reported job relevance or career satisfaction within this sample (Brighouse et al., 2018; Oreopoulos and Salvanes, 2011). In fact, the average job relevance rating was 3.37 for graduates and 3.35 for non-graduates on a 5-point Likert scale, t(2696) = 0.27, p = 0.742. Appendix Tables A1 and A2 show the full categorical breakdowns of the job relevance and career satisfaction variables by educational status.

Table 2

Table 2. Employment status of graduates and non-graduates.

4.2 Economic value of a bachelor’s degree by field of study

Across fields of study, several variations emerged in the economic returns of bachelor’s degrees. A one-way ANOVA showed statistically significant differences in both starting salaries, F(3, 1,660) = 556.60, p < 0.001, and current salaries, F(3, 1,660) = 349.70, p < 0.001. Graduates in STEM reported the highest starting salaries ($72,910.26), followed by those in business ($64,102.87), humanities ($45,077.44), and social sciences ($45,238.28). And post hoc pairwise comparisons using Tukey’s HSD confirmed that inter-field differences were statistically significant (p < 0.001). A similar pattern emerged in current salary data: STEM graduates reported the highest average income ($97,204.38), followed by those with business degrees ($89,154.14), humanities ($70,088.39), and social sciences ($70,120.20). These results align with prior studies indicating that field of study is a strong predictor of post-college earnings (Altonji et al., 2012; Kim et al., 2015; Webber, 2014).

A follow-up regression analysis controlling for gender and race confirmed that academic major remains a strong predictor of current salary, accounting for 42.5% of the variance, F(8, 1,655) = 153.0, p < 0.001, R² = 0.425. Despite these income disparities, career outcomes and subjective job assessments did not change significantly across disciplines. Full-time employment rates ranged from 67.9 to 74.6% [χ²(6) = 6.83, p = 0.337], whereas career satisfaction (Table 3) and perceived job relevance (Table 4) remained relatively stable. The consistency of job satisfaction and perceived relevance across fields indicates that these subjective outcomes did not vary systematically with salary differences across majors. The consistency of job satisfaction and perceived relevance across fields indicates that these subjective outcomes did not vary systematically with salary differences across majors. Graduates in lower-paying disciplines reported levels of perceived alignment comparable to those in higher-paying fields.

Table 3

Table 3. Career satisfaction ratings for different fields of study.

Table 4

Table 4. Job relevance ratings for different fields of study.

4.3 Gender and racial disparities in degree value

Statistical analyses revealed that gender and race independently influenced the monetary returns of a bachelor’s degree. Indeed in the full-sample regression model (Table 5), men earned an average of $4,801.60 more than women, when field of study or academic major and race are controlled (p < 0.001). Compared to White graduates, Black graduates earned $6,289.70 less, and Hispanic graduates earned $5,984.60 less—both differences statistically significant at p < 0.001. These results align with broader labor market evidence that wage disparities persist across racial and gender strata, even when education levels are equalized (Chetty et al., 2020; Houle and Addo, 2019). But interaction effects did not reach statistical significance: two-way ANOVAs showed no significant interaction between field of study and gender [F(3, 1,656) = 0.404, p = 0.750], nor between field of study and race [F(12, 1,644) = 1.083, p = 0.371]. In particular, none of the three-way interaction terms (i.e., race × gender × field of study) in the expanded regression model was statistically significant (p > 0.05). Within this sample, race and gender effects on salary were observed independently, with no statistically detectable interaction with field of study.

Table 5

Table 5. Regression effects of field of study on current salary when gender and race are controlled.

Complementary chi-square tests were used to assess whether employment status varied across demographic groups within fields of study. No statistically significant differences in employment status by gender were detected within any major field—business [χ²(2) = 0.302, p = 0.860], humanities [χ²(2) = 2.060, p = 0.357], social sciences [χ²(2) = 2.135, p = 0.344], or STEM [χ²(2) = 0.310, p = 0.856]. Similarly, no statistically significant associations between employment status and race were detected across the same fields: business [χ²(8) = 4.990, p = 0.759], humanities [χ²(8) = 2.266, p = 0.972], social sciences [χ²(8) = 7.428, p = 0.491], and STEM [χ²(8) = 2.343, p = 0.969].

Perceptions of career satisfaction and advancement potential revealed further patterns of inequality. Among STEM graduates, the proportion rating barriers to advancement as high (4) or very high (5) was comparable across gender and race: 29.2% of women, 30.1% of men, 28.9% of White graduates, and 29.2% of Black graduates. In the humanities, high or very high satisfaction ratings were reported by 46.6% of women, 40.6% of men, 42.9% of White graduates, and 48.4% of Black graduates. In contrast, STEM figures were far more consistent: 43.1% for both men and women, 43.7% for White graduates and 36.9% for Black graduates. To better isolate early-career disparities, a separate regression analysis was carried out with respondents who graduated within the past 2 years. The model was statistically significant, as it explained 41.7% of the variances in current salaries [F(8, 491) = 43.82, p < 0.001, R² = 0.417]. Field of study, gender, and indicators for Black and Hispanic ethnicity emerged as statistically significant predictors (p < 0.05). Relative to STEM graduates, business majors earned $9,495.00 less, humanities majors $26,607.50 less, and social science majors $26,662.90 less, on average. Relative to women, men earned $4,332.40 more when race and academic major were held constant. And relative to their White peers, Black graduates earned $5,487.70 less, and Hispanic graduates $6,185.50 less—even after holding field of study and gender constant.

As with the full-sample model, the lack of statistically significant interaction terms in this early-career regression (p > 0.05) means that race and gender influence income independently and cumulatively, rather than through interactions or variations across fields of study. Figure 2 shows these intersecting disparities in reported career satisfaction across academic majors.

Figure 2

Figure 2. Bar graphs comparing career satisfaction ratings between men and women for humanities and STEM fields of study.

Consistent with the regression results, chi-square analyses revealed no statistically significant associations between employment status and either race or gender within any field of study (all p > 0.34). This indicates that no statistically significant differences in access to full-time employment were detected across identity or academic major within this sample. Still, modest differences in career satisfaction across race and gender, such as lower ratings between Black STEM graduates (36.9%) and their White counterparts (43.7%), point to inequities that may not be fully captured by salary or employment data alone. Figure 3 shows these satisfaction differentials across demographic groups and offers a fuller view of post-graduation experiences (Xu, 2017).

Figure 3

Figure 3. Bar graphs comparing career satisfaction ratings across races for humanities and STEM fields of study.

Among graduates who had completed their degrees within the past 2 years (n = 500), regression analysis produced findings that closely mirrored those of the full sample. The early-career graduate subsample consisted of the first 500 survey respondents identified as having graduated within 2 years of the data collection period. As shown in Table 6, STEM remained the highest-earning major, with all other majors earning significantly less (p < 0.001). The model explained 41.7% of the variance in current salary, F(8, 491) = 43.82, p < 0.001, R² = 0.417. On average, male graduates earned $4,332.40 more than female graduates, while Black and Hispanic graduates earned $5,487.70 and $6,185.50 less than their White counterparts, respectively (p < 0.01). No statistically significant interaction effects were found between field of study, race, and gender, and no variation was detected in employment status across these dimensions—patterns that aligned with the full-sample model. Career satisfaction and job relevance ratings among early-career graduates also displayed the same cross-field stability observed in the broader sample (see Appendix Tables A3 and A4). For example, 48.0% of humanities majors reported high or very high career satisfaction, compared to 45.9% in STEM and 44.0% in business—which means that even in the early years of post-graduate employment, subjective well-being does not consistently reflect income.

Table 6

Table 6. Effects of field of study on current salary when gender and race are controlled.

4.4 Summary of key trends

Overall, the data examined showed that earning a bachelor’s degree was associated with higher salaries and greater access to full-time employment, consistent with prior research on the economic correlates of postsecondary education (Ali and Jalal, 2018; Dadgar and Trimble, 2015). These associations were not uniform across academic majors or demographic groups. Graduates in STEM and business fields earned significantly more than their peers in the humanities and social sciences, reflecting well-documented patterns in returns by field of study (Emerson and McGoldrick, 2019). Persistent income differences were also observed across race and gender. Men earned more than women, and White graduates earned more than Black and Hispanic graduates, even when field of study was taken into account. These differences were evident among early-career graduates and were also present in the full sample, aligning with prior findings that a bachelor’s degree alone does not ensure equal economic outcomes across social groups (McGuinness, 2006; Oreopoulos and Salvanes, 2011).

By contrast, employment status did not vary significantly by race or gender within fields of study, and no statistically detectable interaction effects emerged between demographic characteristics and academic major. Within this sample, observed differences associated with race and gender did not vary systematically across fields, indicating that disparities in earnings were not accompanied by corresponding variation in full-time employment across disciplines. At the same time, modest differences in reported career satisfaction were observed across demographic groups. For example, lower satisfaction ratings among some Black STEM graduates suggested variation in subjective post-graduation experiences that was not fully reflected in income or employment measures alone. Taken together, these patterns underscore the multidimensional nature of degree value, which encompasses not only objective labor-market indicators such as salary and employment status but also subjective evaluations of job relevance and career satisfaction (Brighouse et al., 2018; Emerson and McGoldrick, 2019).

5 Discussion

This study used a cross-sectional survey design to examine how economic and career-related outcomes associated with a bachelor’s degree vary across academic disciplines and demographic groups. Drawing on data from 2,698 U.S. adults—including both graduates and non-graduates—the analyses focused on salary, employment status, perceived job relevance, and career satisfaction to assess whether the economic advantages commonly associated with postsecondary education are distributed consistently across race, gender, and field of study. Framed within Human Capital Theory (HCT), which links educational attainment to labor-market productivity and earnings (Oreopoulos and Petronijevic, 2013), the findings were interpreted as descriptive patterns rather than as causal estimates of educational returns.

Consistent with central claims of HCT, graduates reported higher starting and current salaries and were more likely to be employed full-time than non-graduates, indicating that a bachelor’s degree remains associated with favorable labor-market outcomes (Kim et al., 2015). These associations were also observed among respondents surveyed within 2 years of degree completion, suggesting that salary and employment differences between graduates and non-graduates are present early in post-graduation trajectories. At the same time, subjective outcomes such as job relevance and career satisfaction did not differ systematically by educational attainment, indicating that higher earnings and employment rates were not associated with higher reported alignment or fulfillment within this sample.

Differences by field of study further illustrated the uneven distribution of economic outcomes among degree holders. Graduates in STEM and business fields consistently reported higher earnings than those in the humanities and social sciences, even when demographic characteristics were taken into account, corroborating prior research identifying academic major as a key correlate of income differences (Altonji et al., 2012; Webber, 2014). Despite these disparities, reported levels of job satisfaction and perceived job relevance remained relatively stable across disciplines. This pattern suggests that subjective evaluations of work may reflect dimensions of career experience not captured by salary alone, including interest alignment, autonomy, or perceived meaning (Oreopoulos and Salvanes, 2011; Xu, 2017).

The analyses also revealed persistent salary differences associated with race and gender. Men earned more than women, and White graduates earned more than Black and Hispanic graduates, even after controlling for field of study and employment status. These patterns mirror disparities documented in national longitudinal datasets and underscore enduring inequities in labor-market outcomes that extend beyond educational attainment (Chetty et al., 2020; Houle and Addo, 2019). Within this sample, race and gender were independently associated with earnings, with no statistically detectable interaction with academic major. This finding indicates that observed income differences did not vary systematically across fields of study, rather than reflecting discipline-specific disparities. Subjective indicators further nuanced this picture: while reported barriers to advancement were similar across gender groups within STEM, Black graduates reported slightly higher perceived obstacles than White graduates, pointing to disparities that may not be fully reflected in salary or employment measures alone (Houle and Addo, 2019).

Taken together, these findings reinforce the value of examining both objective and subjective dimensions of postsecondary outcomes. While a bachelor’s degree remains associated with higher earnings and employment, the distribution of these benefits varies meaningfully across academic fields and demographic groups, and financial advantages do not necessarily correspond to higher perceived fulfillment or relevance. These patterns highlight the importance of policy approaches that consider not only access to and completion of postsecondary education, but also the differentiated labor-market experiences that follow graduation. Interventions aimed at reducing disparities may therefore require attention to field-specific pathways, early-career transitions, and the non-monetary aspects of work that shape long-term satisfaction and opportunity.

5.1 Limitations

Several limitations should be considered when interpreting these findings. First, the cross-sectional design limits inference to observed associations and does not permit causal conclusions about the relationship between educational attainment and labor-market outcomes. Without longitudinal data, it is not possible to assess how differences in earnings, employment, or satisfaction evolve across career stages or whether observed patterns persist, attenuate, or change over time (Brand and Xie, 2010). Second, key outcome measures relied on self-reported data, including salary and subjective assessments of job relevance and career satisfaction. Although salary information was collected using open-ended responses and subjective measures were drawn from validated survey instruments, self-report data may be affected by recall error, response bias, or variation in interpretation across individuals and contexts (Oreopoulos and Salvanes, 2011).

Third, the analyses did not include institutional or contextual variables such as college selectivity, tuition costs, industry of employment, or geographic labor-market conditions, all of which are known to shape post-graduation outcomes (Altonji et al., 2012; Kim et al., 2015). The absence of these factors limits the ability to disentangle institutional influences from broader structural or market-level dynamics. Fourth, even though the sample was stratified by race, gender, educational attainment, and region, statistical power was limited for detecting higher-order interaction effects within smaller subgroups, such as specific race–gender–field combinations. Similarly, the early-career subsample consisted of the first 500 respondents who met the eligibility criterion, which may overrepresent individuals who were more responsive or engaged at the time of data collection.

Finally, as with many online survey studies, recruitment through a digital panel introduces the possibility of selection bias. While the sample was designed to support descriptive subgroup comparisons rather than population-level estimation, findings may not generalize fully to all U.S. college graduates and non-graduates (Webber, 2014).

5.2 Practical implications

The findings of this study have implications for higher education policy, institutional practice, and student advising. Observed salary differences across academic majors highlight the importance of transparent advising that communicates how earnings outcomes tend to vary by field of study, particularly for first-generation and historically underrepresented students (Altonji et al., 2012; see also Kim et al., 2015). At the same time, the absence of corresponding differences in reported job relevance or career satisfaction suggests that financial indicators alone do not capture the full range of outcomes students associate with degree value. Advising models may therefore benefit from balancing information about expected earnings with structured reflection on personal interests, perceived fit, and longer-term career satisfaction (Oreopoulos and Petronijevic, 2013).

Persistent salary differences associated with race and gender, even when field of study and educational attainment are taken into account, also indicate limits to advising-based interventions alone. These patterns underscore the relevance of institutional efforts aimed at improving access to internships, mentoring, and career development resources, particularly in fields associated with higher economic returns (Houle and Addo, 2019; Xu, 2017). At the policy level, the findings are consistent with calls for stronger monitoring of labor-market outcomes disaggregated by demographic group and academic field, as well as continued attention to equity in hiring, compensation, and promotion practices (Chetty et al., 2020).

Federal financial aid policies may also play a role in shaping students’ educational choices and workforce alignment. Prior research suggests that financial incentives, including need-based grants, can influence major selection in areas such as STEM and business (Bettinger et al., 2012; Zhao et al., 2024). Any such policy approaches, however, require careful consideration to avoid unintended consequences, including narrowing educational choice or diminishing the perceived value of other fields of study. Policies linking financial aid to specific majors are most appropriately considered as part of broader higher-education funding and workforce development strategies.

Finally, the patterns documented here point to the importance of continued empirical work capable of capturing how educational pathways and labor-market outcomes unfold over time. Longitudinal and mixed-methods studies that incorporate institutional characteristics, such as selectivity, cost of attendance, and regional labor conditions, would allow for more precise modeling of postsecondary returns (Brand and Xie, 2010). Future research that draws on larger and more intersectionally representative samples may also provide clearer insight into how race, gender, and other social factors jointly shape post-graduation experiences (Brighouse et al., 2018; Xu, 2017).

6 Conclusion

This study examined variation in labor-market outcomes associated with a bachelor’s degree by analyzing income, employment status, and career satisfaction among 2,698 adults drawn from a stratified sample. Across the sample, degree holders reported higher earnings and greater access to full-time employment than non-graduates, patterns that are consistent with longstanding empirical work linking postsecondary education to economic advantage (Altonji et al., 2012; Oreopoulos and Petronijevic, 2013). At the same time, these associations were not uniform. Differences by academic major, race, and gender were evident, with graduates in STEM and business fields reporting substantially higher earnings than those in the humanities and social sciences, even as reported levels of career satisfaction remained similar across fields. These findings align with prior evidence of discipline-specific wage differentials and underscore the importance of evaluating degree value using both economic and experiential indicators (Altonji et al., 2012; Webber, 2014).

Subjective outcomes such as job satisfaction and perceived alignment exhibited relative stability across academic fields, suggesting that financial compensation and experiential fulfillment do not necessarily covary. Consistent with earlier research, reported satisfaction appeared to reflect factors such as interest alignment, autonomy, and perceived meaning that extend beyond income alone (Oreopoulos and Salvanes, 2011; Xu, 2017). In contrast, economic outcomes displayed persistent stratification by race and gender. Women and graduates from historically marginalized racial and ethnic groups reported lower earnings than White men, even when field of study and employment status were taken into account. These patterns mirror disparities documented in broader labor-market research and indicate that educational attainment alone does not neutralize entrenched inequalities in economic returns (Blau and Kahn, 2017).

Viewed together, the coexistence of stable subjective evaluations and unequal financial outcomes suggests that the bachelor’s degree functions simultaneously as a vehicle for opportunity and as a credential shaped by stratified labor-market conditions. This dual character complicates simplified narratives that frame higher education as a uniformly equalizing investment and highlights the importance of examining how economic benefits are distributed across social groups (Chetty et al., 2020; Oreopoulos and Petronijevic, 2013). From a research perspective, these findings reinforce the value of moving beyond single-outcome assessments of educational returns. Future work would benefit from longitudinal and mixed-methods designs capable of tracing how economic and experiential dimensions of postsecondary outcomes unfold across career stages and institutional contexts.

In theoretical terms, the study contributes a descriptive account of the bachelor’s degree as a credential whose observed value varies systematically with field of study and demographic position. Rather than attributing outcomes solely to individual educational investment, the findings point to the role of broader socioeconomic structures that shape how credentials are translated into opportunity (Blau and Kahn, 2017; Brand and Xie, 2010; Oreopoulos and Salvanes, 2011). By integrating objective labor-market indicators with subjective evaluations of work, this study offers a more nuanced empirical foundation for understanding what a bachelor’s degree affords—and for whom—in contemporary labor markets.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Institutional Review Board of the University of Nevada, Las Vegas (UNLV Office of Research Integrity). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

DA: Writing – original draft, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This study was funded by Nevadans First, a non-profit promoting educational opportunity, through a grant approved by an independent advisory board.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2025.1727346/full#supplementary-material

References

Ali, M. S., and Jalal, H. (2018). Higher education as a predictor of employment: the world of work perspective. Bull. Educ. Res., 40, 79–90. Available online at: https://www.researchgate.net/publication/347885454_Higher_Education_as_a_Predictor_of_Employment_The_World_of_Work_Perspective

Google Scholar

Alon, S., and Tienda, M. (2005). Assessing the “mismatch” hypothesis: differences in college graduation rates by institutional selectivity. Sociol. Educ. 78, 294–315. doi: 10.1177/003804070507800402

Crossref Full Text | Google Scholar

Altonji, J. G., Arcidiacono, P., and Maurel, A. (2016). “The analysis of field choice in college and graduate school” in Handbook of the economics of education. eds. E. A. Hanushek, S. Machin, and L. Woessmann (Amsterdam: Elsevier), 305–396.

Google Scholar

Altonji, J. G., Blom, E., and Meghir, C. (2012). Heterogeneity in human capital investments: high school curriculum, college major, and careers. Annu. Rev. Econ. 4, 185–223. doi: 10.1146/annurev-economics-080511-110908

Crossref Full Text | Google Scholar

Arcidiacono, P. (2004). Ability sorting and the returns to college major. J. Econom. 121, 343–375. doi: 10.1016/j.jeconom.2003.10.010

Crossref Full Text | Google Scholar

Baum, S., Ma, J., and Payea, K. (2013). Education pays 2013: the benefits of higher education for individuals and society. New York, NY: College Board.

Google Scholar

Becker, G. S. (2008). Human capital: a theoretical and empirical analysis, with special reference to education. 3rd Edn. Chicago, IL: The University of Chicago Press.

Google Scholar

Belfield, C. R., and Bailey, T. (2017). The labor market returns to sub-baccalaureate college: a review. New York, NY: Center for Analysis of Postsecondary Education and Employment.

Google Scholar

Bettinger, E. P., Long, B. T., Oreopoulos, P., and Sanbonmatsu, L. (2012). The role of application assistance and information in college decisions: results from the H&R Block FAFSA experiment. Q. J. Econ. 127, 1205–1242. doi: 10.1093/qje/qjs017

Crossref Full Text | Google Scholar

Blau, F. D., and Kahn, L. M. (2017). The gender wage gap: extent, trends, and explanations. J. Econ. Lit. 55, 789–865. doi: 10.1257/jel.20160995

Crossref Full Text | Google Scholar

Brand, J. E., and Xie, Y. (2010). Who benefits most from college? Evidence for negative selection in heterogeneous economic returns to higher education. Am. Sociol. Rev. 75, 273–302. doi: 10.1177/0003122410363567,

PubMed Abstract | Crossref Full Text | Google Scholar

Brighouse, H., Swift, A., Ladd, H. F., and Loeb, S. (2018). Educational goods: Values, evidence, and decision making. Chicago, IL: The University of Chicago Press.

Google Scholar

Carnevale, A. P., Rose, S. J., and Cheah, B. (2011). The college payoff: education, occupations, lifetime earnings. Washington, D.C.: Georgetown University Center on Education and the Workforce.

Google Scholar

Chetty, R., Hendren, N., Jones, M. R., and Porter, S. R. (2020). Race and economic opportunity in the United States: an intergenerational perspective. Q. J. Econ. 135, 711–783. doi: 10.1093/qje/qjz042

Crossref Full Text | Google Scholar

Cooper, P. (2021). Is college worth it? A comprehensive return on investment analysis. Austin, TX: The Foundation for Research on Equal Opportunity.

Google Scholar

Dadgar, M., and Trimble, M. J. (2015). Labor market returns to sub-baccalaureate credentials: how much does a community college degree or certificate pay? Educ. Eval. Policy Anal. 37, 399–418. doi: 10.3102/0162373714553814

Crossref Full Text | Google Scholar

Emerson, T. L. N., and McGoldrick, K. (2019). Switching majors – into and out of economics. J. Econ. Educ. 50, 321–332. doi: 10.1080/00220485.2019.1618763

Crossref Full Text | Google Scholar

England, P. (2010). The gender revolution: uneven and stalled. Gender Soc. 24, 149–166. doi: 10.1177/0891243210361475

Crossref Full Text | Google Scholar

Ghosh, R. (2014). Antecedents of mentoring support: a meta-analysis of individual, relational, and structural or organizational factors. J. Vocat. Behav. 84, 367–384. doi: 10.1016/j.jvb.2014.02.009

Crossref Full Text | Google Scholar

Goldrick-Rab, S. (2010). Challenges and opportunities for improving community college student success. Rev. Educ. Res. 80, 437–469. doi: 10.3102/0034654310370163

Crossref Full Text | Google Scholar

Houle, J. N., and Addo, F. R. (2019). Racial disparities in student debt and the reproduction of the fragile black middle class. Sociol. Race Ethn. 5, 562–577. doi: 10.1177/2332649218790989

Crossref Full Text | Google Scholar

Kim, C., Tamborini, C. R., and Sakamoto, A. (2015). Field of study in college and lifetime earnings in the United States. Sociol. Educ. 88, 320–339. doi: 10.1177/0038040715602132,

PubMed Abstract | Crossref Full Text | Google Scholar

Marginson, S. (2019). The dream is over: the crisis of Clark Kerr’s California idea of higher education. Oakland, CA: University of California Press.

Google Scholar

McGuinness, S. (2006). Overeducation in the labour market. J. Econ. Surv. 20, 387–418. doi: 10.1111/j.0950-0804.2006.00284.x

Crossref Full Text | Google Scholar

Mulder, L., Akwiwu, E. U., Twisk, J. W. R., Koster, A. S., Ravesloot, J. H., Croiset, G., et al. (2023). Inequality of opportunity in selection procedures limits diversity in higher education: an intersectional study of Dutch selective higher education programs. PLoS One 18:e0292805. doi: 10.1371/journal.pone.0292805,

PubMed Abstract | Crossref Full Text | Google Scholar

National Center for Education Statistics (2020). National postsecondary student aid study (NPSAS:20) [Data file and codebook]. Washington, DC: U.S. Department of Education.

Google Scholar

National Student Clearinghouse Research Center 2024 Some college, no credential student outcomes: 2024 Report for the nation and the states. Available online at: https://nscresearchcenter.org/some-college-no-credential/ (Accessed October 10, 2025).

Google Scholar

Oreopoulos, P., and Petronijevic, U. (2013). Making college worth it: a review of the returns to higher education. Futur. Child. 23, 41–65. doi: 10.1353/foc.2013.0001,

PubMed Abstract | Crossref Full Text | Google Scholar

Oreopoulos, P., and Salvanes, K. G. (2011). Priceless: the nonpecuniary benefits of schooling. J. Econ. Perspect. 25, 159–184. doi: 10.1257/jep.25.1.159

Crossref Full Text | Google Scholar

Perna, L. W. (2005). The benefits of higher education: sex, racial/ethnic, and socioeconomic group differences. Rev. High. Educ. 29, 23–52. doi: 10.1353/rhe.2005.0073

Crossref Full Text | Google Scholar

Pew Research Center 2016 The state of American jobs. Available online at: https://www.pewresearch.org/social-trends/2016/10/06/the-state-of-american-jobs/ (Accessed October 10, 2025).

Google Scholar

Rothstein, J., and Rouse, C. E. (2011). Constrained after college: student loans and early-career occupational choices. J. Public Econ. 95, 149–163. doi: 10.1016/j.jpubeco.2010.09.015,

PubMed Abstract | Crossref Full Text | Google Scholar

Sloane, C. M., Hurst, E. G., and Black, D. A. (2021). College majors, occupations, and the gender wage gap. J. Econ. Perspect. 35, 223–248. doi: 10.1257/jep.35.4.223

Crossref Full Text | Google Scholar

Tamborini, C. R., Kim, C., and Sakamoto, A. (2015). Education and lifetime earnings in the United States. Demography 52, 1383–1407. doi: 10.1007/s13524-015-0407-0,

PubMed Abstract | Crossref Full Text | Google Scholar

Webber, D. A. (2014). The lifetime earnings premia of different majors: correcting for selection based on cognitive, noncognitive, and unobserved factors. Labour Econ. 28, 14–23. doi: 10.1016/j.labeco.2014.03.009

Crossref Full Text | Google Scholar

Xu, Y. J. (2017). Attrition of women in STEM: examining job/major congruence in the career choices of college graduates. J. Career Dev. 44, 3–19. doi: 10.1177/0894845316633787

Crossref Full Text | Google Scholar

Zhao, T., Perez-Felkner, L., and Hu, S. (2024). The impact of merit aid on STEM major choices: a propensity score approach. Educ. Eval. Policy Anal. 47, 939–959. doi: 10.3102/01623737241254842,

PubMed Abstract | Crossref Full Text | Google Scholar