Major Payoffs: Postcollege Income, Graduate School, and the Choice of "Risky" Undergraduate Majors - David B. Monaghan
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688445
research-article2017
SPXXXX10.1177/0731121416688445Sociological PerspectivesMonaghan and Jang
Education
Sociological Perspectives
2017, Vol. 60(4) 722–746
Major Payoffs: Postcollege Income, © The Author(s) 2017
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DOI: 10.1177/0731121416688445
https://doi.org/10.1177/0731121416688445
of “Risky” Undergraduate Majors journals.sagepub.com/home/spx
David Monaghan1 and Sou Hyun Jang2
Abstract
Although the bachelor’s degree is considered the “great equalizer,” returns vary substantially by
field of study, particularly in the years immediately following graduation. In the first section of
our analysis, we study the varying labor market experiences of recent graduates with different
majors. We build on prior research by more fully accounting for the complicating role of graduate
school attendance in the relationship between majors and income. We find some majors to be
distinctly “risky,” exposing their holders to heightened risk of low income and unemployment
during the postcollege transition. Those who select such majors are much more likely to later
enroll in graduate school. After 10 years, graduate degrees mitigate, but do not entirely erase,
major-based income disparities. We use these findings in the second section to explore the
determinants of major choice among first-time freshmen. Female and higher socioeconomic
status (SES) students are more likely to select risky majors, but the latter relationship is entirely
explained by academic and institutional variables. In contrast to prior research, we find strong
institutional effects on major choice, with those attending selective colleges, smaller institutions,
and institutions with fewer low-SES students more likely to select risky and graduate-school-
associated majors, net of individual-level factors. We conclude by discussing the implications of
our findings for the situation of the arts and sciences fields in the era of mass enrollment.
Keywords
socioeconomic stratification, higher education, college major, graduate degrees
Introduction
In the wake of the Great Recession, social scientists have raised alarms about college graduates’
transition into the labor market (Arum and Roksa 2014; Newman 2012). A bachelor’s degree is
expected to facilitate access to remunerative jobs, yet many recent graduates struggle to secure
such positions (Stone, Van Horn, and Zukin 2012). In one recent survey, 39 percent of recent
graduates said their first job paid far less than expected, and 23 percent said it did not provide
health insurance (Godofsky, Zukin, and Van Horn 2011). Inability to find a foothold in the labor
market can lead youth to delay marriage, childbearing, or homeownership, or to return to living
1University of Wisconsin–Madison, Madison, WI, USA
2City University of New York, New York, NY, USA
Corresponding Author:
David Monaghan, The Wisconsin HOPE Lab, School of Education, University of Wisconsin–Madison, L139 Education,
1000 Bascom Mall, Madison, WI 53706, USA.
Email: dmonaghan@wisc.eduMonaghan and Jang 723
with parents (Carnevale, Hanson, and Gulish 2013). Difficulty in securing adequate employment
is rendered more perilous by the need to service student loan debts, which are held by 61 percent
of recent graduates (The College Board 2015).
One consequential factor in this regard is one’s undergraduate major. In the years immediately
after graduation, graduates of some majors are much more likely to endure low incomes and
unemployment than others (Carnevale, Cheah, and Strohl 2012; Staklis and Skomsvold 2014).
Although given sufficient time most college graduates appear to prosper regardless of major
(Choy and Bradburn 2008), early years are fraught with risk, and some majors reveal themselves
to be particularly “risky”. Perhaps for this reason, the postcollege transition sees many evincing
buyer’s remorse: In a recent study, 37 percent of recent graduates reported wishing that they had
been more careful in choosing a major (Stone et al. 2012).
But which majors are risky, and who chooses them? Although American colleges portray the
choice of major as a highly individualistic process, research has suggested that it is influenced by
gender (Daymont and Andrisani 1984; Ma 2009; Turner and Bowen 1999), race (Staniec 2004),
nativity (Ma 2009; Min and Jang 2015), socioeconomic status (SES) (Davies and Guppy 1997;
Goyette and Mullen 2006; Ma 2009), and performance on math and verbal aptitude tests
(Arcidiacono 2004). Researchers have modeled students’ choices in terms of majors’ expected
and average incomes (Beffy, Fougere, and Maurel 2012; Davies and Guppy 1997), but not with
explicit reference to the risk to which majors expose graduates.
We first investigate how early labor market experiences of college graduates differ by college
major, focusing on the associated risk of low income and prolonged unemployment. As individu-
als may avoid or respond to poor employment outcomes by obtaining further education, we
investigate how graduate school enrollment rates vary by major and how further degrees mitigate
major-based income disparities over time. We then use these findings to model the major choices
of incoming freshmen.
Conceptual Background and Prior Research
College Majors, Jobs, and Income
The income of college graduates varies substantially by major (Kim, Tamborini, and Sakamoto
2015), but why this occurs is not agreed on. Observed income differences could simply reflect
pre-existing “ability” difference among those who choose various majors (Paglin and Rufolo
1990). However, income differences are only partially attenuated when academic ability and col-
lege selectivity are controlled (Grogger and Eide 1995). Another common account holds that
majors imbue students with different quantities of scarce educational resources (i.e., human capi-
tal). Jeff Grogger and Eric Eide (1995) focus on majors’ differential augmentation of quantitative
skills, while Herman G Van de Werfhorst and Gerbert Kraaykamp (2001) contend that majors
instill students with differing admixtures of four species of educational resources (economic,
cultural, communicative, and technical).
An alternative approach, suggested by Weberian theory, takes into account the legal, institu-
tional, and customary linkages between college majors and remunerative occupations. Although,
relative to other rich countries, American educational institutions are weakly articulated with the
labor market (Kerckhoff 2001), majors vary in the degree to which they offer a well-established
path to a set of occupations, and in the remuneration of occupations to which they render access.
This is true among more career-oriented majors (contrast engineering with fitness and leisure stud-
ies), but the starker contrast is between these fields and the arts and sciences collectively.
Vocationally oriented majors arose at the impetus of either labor market actors—occupational
associations seeking to restrict access to a field, or industry leaders wishing to regularize the sup-
ply of trained workers (Brown 1995; Khurana 2010)—or colleges attempting to boost enrollments724 Sociological Perspectives 60(4)
by creating programs aligned with growing occupations (Brint et al. 2011). In both scenarios,
interests existed to nurture pathways from specific majors to specific occupations. Conversely,
arts and sciences disciplines arose through logics mostly internal to the academic field, with little
reference to the occupational structure (Machlup 1984). Generally, it is not until the graduate level
that these programs provide routes to occupations—for example, research staff positions and pro-
fessorships. Consequently, bachelors’ degree holders from these majors are more likely to work in
unrelated fields and to have lower starting salaries (Robst 2007; Staklis and Skomsvold 2014).
These considerations suggest:
Hypothesis 1: Early after college, those whose bachelor’s degrees are in the arts and sciences
fields will earn lower incomes and be more likely to experience unemployment than practical
arts majors.
The Role of a Graduate School
That education may be continued beyond the bachelor’s degree complicates the relationship
between majors and income. Graduate degrees boost incomes, and the probability of attending a
graduate school varies across undergraduate majors (Mullen, Goyette, and Soares 2003; Zhang
2005). But what mechanism links majors to graduate school attendance? Eric Eide and Geetha
Waehrer (1998) argue that majors vary in both labor market value and “option value”: the facili-
tation of graduate school enrollment—and thus higher wages. These two values conflict, as high
immediate wages create high opportunity costs for attending a graduate school. Following this,
Moohoun Song, Peter F. Orazem, and Darin Wohlgemuth (2008) argue that high returns to quan-
titative skills depresses graduate school attendance among graduates from quantitative fields,
despite large potential returns to advanced degrees.
Incorporating institutional arrangements is again helpful. Some majors have become de facto
professional school preparatory programs—for example, biology (medical school), history, and
political science (law school). In other cases, laws mandate graduate degrees to obtain or persist
in occupations toward which majors are oriented (e.g., education and social work). For the
remaining majors, undergraduate-graduate pathways are not so structured. Some students may
select their undergraduate major with a mind to attending (or avoiding) graduate school; others
may initially give little consideration to the need for further education. Postgraduate labor market
experiences may then crystallize or revise prior inclinations toward graduate study, or point to its
necessity. It follows that majors that do not provide links to remunerative occupations will be
associated with greater graduate school attendance.
This suggests that:
Hypothesis 2: Majors that have lower incomes for bachelor’s degree holders will have higher
rates of graduate school enrollment.
Hypothesis 3: Graduate school will partly compensate for lower incomes of “riskier” majors.
The Choice of Major: Student Background Characteristics
Sociologists have long alleged lower SES students to be more likely to pursue higher education
primarily for economic reasons (Clark and Trow 1966; Katchadourian and Boli 1985). Yingyi
Ma (2009), drawing on John H. Goldthorpe (1996), suggests why this might be. Goldthorpe
argues that that the modal aspiration for all individuals, regardless of SES, is avoiding downward
mobility. For Ma, this leads lower SES students to be guarded in their aspirations, preferring
fields that provide a surer link to stable employment, while privileged students seek higher levels
of attainment to maintain their relative advantage. They are thus more likely to select majors thatMonaghan and Jang 725
assist entry into a graduate school. In addition, family resources permit freedom to consider
“interesting” majors, heedless of risk.
Empirical research supports the intuition that working-class students opt for “practical”
majors over the arts and sciences (Davies and Guppy 1997; Goyette and Mullen 2006; Ma 2009).
Lower SES students also appear more responsive to majors’ changing labor market value (Long,
Goldhaber, and Huntington-Klein 2015) and are more likely to switch majors when informed of
higher paying alternatives (Hastings, Neilson, and Zimmerman 2015). This logic has been
extended to disadvantaged minorities. Yu Xie and Kimberly Goyette (2003) interpret the greater
inclination of Asians relative to whites toward remunerative majors as “strategic adaptation” for
upward mobility (see also Ma 2009; Min and Jang 2015). Research also finds black males (rela-
tive to whites) to be more likely to enter STEM fields, net of SES and academic preparation
(Staniec 2004).
But in the case of gender, the historically disadvantaged group appears drawn toward less
lucrative majors (Ma 2009). There are three theoretical accounts of this relationship. The first,
which holds that gender-based differences (innate or acquired) in quantitative reasoning skills
lead women away from certain majors (Paglin and Rufolo 1990), is contradicted by research
showing that gender differences in majors persist when controlling for quantitative ability (Turner
and Bowen 1999). The second postulates that because women expect childbearing to interrupt
careers, they select majors imparting general, transferrable, but lower return, skills (Daymont and
Andrisani 1984; Tam 1997). The final theory argues that gender socialization leads women to
avoid high-paying fields socially constructed as “male” (Ochsenfeld 2014).
The foregoing suggests that:
Hypothesis 4: Higher SES, white, and nonimmigrant students will be more likely to select
riskier and graduate school associated majors than lower SES, minority, and immigrant or
second-generation students.
Hypothesis 5: Female students will be more likely to select lower paid, riskier majors.
Choosing a Major: The Role of Institutions
This is further complicated by the facts that student social background influences the selectiv-
ity of one’s college (Reardon, Baker, and Klasik 2012), and that institutions do not offer
uniform sets of majors. There is a homology in the academy between the prestige bestowed
on an area of knowledge—with “applied” knowledge devalued as derivative—and the pres-
tige of the institutions that disseminate that knowledge. And the prestige of institutions cor-
responds to the social origins and academic preparation of the students they enroll. Thus,
though their roots in American higher education date back at least to the 1862 Morrill Act,
practical arts fields have been only reluctantly and partially adopted by leading institutions
despite their overall growth (Brint et al. 2011; Brint et al. 2005). Consequently, increasingly
elite students attend institutions that primarily truck in the arts and sciences, while poorer and
less distinguished students attend institutions in which practical arts dominate.1 The expan-
sion of higher education through a progressive incorporation of lower SES and less prepared
students has been concurrent with differentiation into “academic” and “vocational” foci at the
institutional level.
This differentiation may be supply or demand driven. In creating and promoting vocational
majors, less selective colleges could be reflecting the preferences of likely students. As less
selective schools must compete with each other for applicants, their curricula may be more
responsive to market forces (Brint et al. 2012). Conversely, leading institutions maximize pres-
tige through recruiting the most sought-after students and faculty, amassing awards and research
funding. As their graduates may rely on value of their degrees as signals (Gerber and Cheung726 Sociological Perspectives 60(4) 2008; Mullen 2010), elite colleges can largely dispense with teaching immediately applicable job skills, freeing faculty to focus on the production and teaching of disciplinary knowledge: Hypothesis 6: Students attending selective institutions will be more likely to select riskier and graduate-school-oriented majors. Data and Method Data We use data from two surveys conducted by the National Center for Education Statistics (NCES): the first iteration of the Baccalaureate and Beyond Longitudinal Study (B&B: 93/03) and the 2004–2009 Beginning Postsecondary Students Longitudinal Study (BPS: 04/09). The B&B fol- lowed a representative sample of over 11,000 individuals who earned bachelor’s degrees in 1993 and re-interviewed them in 1994, 1997, and 2003. Data were gathered on students’ college-going (including major) as well as subsequent labor market experiences and enrollment in further edu- cation. A total of 72 percent of respondents participated in all three waves of the survey. After excluding those missing information on institutional selectivity or income in any survey year, we obtained a sample of 5,160 respondents, and used NCES-provided sampling weights for all anal- yses (NCES 2005). The BPS: 04/09 is a nationally representative sample of over 16,000 first-time freshmen enrolling in postsecondary institutions in the 2003–2004 academic year. Follow-up surveys were carried out in 2006 and 2009. Data were collected on students’ socioeconomic and demographic backgrounds, precollege academic preparation, educational expectations, and the institutions they attended (NCES 2011). In addition, the NCES collected transcripts from all institutions in which respondents enrolled during the six-year study window; data culled from transcripts were part of a supplementary release (NCES 2012) that we merged with the survey response data. We restrict the analysis to students who (1) initially enrolled in public or private nonprofit bachelor’s degree granting institutions, and (2) have a declared major listed on their college transcript. The former restriction is in place because we are interested in the fields of study of bachelor’s-seeking students specifically. The second restriction is made because we consider transcript-listed majors to be more reliable and substantial than major preferences identified in surveys. This does not limit us to students who completed bachelor’s degrees but does eliminate students who did not persist long enough to officially declare a major (about 18% of initial entrants of four-year public and nonprofit schools). We adjust for selective sample attrition through inverse probability weighting (multiplying IPWs by NCES-provided sampling weights). Our BPS sample is 6,720 students.2 The temporal relationship between these two datasets is felicitous for our purposes: B&B respondents’ 10th year on the labor market corresponds with BPS respondents’ entry into higher education. The B&B data thus decently approximate the sort of labor market informa- tion available (statistically or anecdotally) to freshmen entering college in that year. The expected income literature on major choice provides some evidence that recent labor market information is impactful (Beffy et al. 2012; Long, Goldhaber, and Huntington-Klein 2015), and similar strategies to ours have been used in prior research (e.g., Davies and Guppy 1997; Staniec 2004). In both the B&B and BPS surveys, the NCES collapsed majors from students’ transcripts into categories. We further collapsed categories to harmonize them across the two surveys and to ensure that each major had at least 15 cases in the B&B. Nevertheless, our final determination of major categories remains detailed—28 in total. Per-major counts for both datasets appear in Table 1, and a harmonization cross-walk is in the appendix.
Monaghan and Jang 727 Table 1. Major Frequencies in the Baccalaureate & Beyond 1993–2003 and Beginning Postsecondary Students Longitudinal Study 2004–2009. Major B&B BPS Arts & sciences Physical & natural sciences 150 110 Area, ethnic, & gender studies 30 40 Architecture & related 30 40 Social sciences 450 510 Biological sciences 300 410 Liberal arts 100 270 Communications & journalism 320 370 Mathematics & statistics 90 80 History 160 160 Natural resources & conservation 30 50 English 270 210 Philosophy, religious studies, & theology 50 120 Psychology 370 360 Foreign languages & literatures 60 90 Visual & performing arts 210 380 Interdisciplinary studies 50 130 Total arts & sciences 2,660 3,340 Practical arts Engineering 390 380 Computer science 140 140 Engineering technology 40 60 Industrial arts 40 70 Business, management, & marketing 650 1,180 Agriculture & related 60 50 Personal, family, & consumer services 60 150 Parks, recreation, & fitness 20 150 Security & protective services 40 140 Criminal justice, public administration, & social services 110 100 Education 610 450 Health professions 340 520 Total practical arts 2,500 3,380 Total 5,160 6,720 Source. National Center for Education Statistics (NCES; 2005, 2011, 2012). Note. In compliance with NCES requirements, all samples have been rounded to the nearest 10. B&B = Baccalaureate and Beyond; BPS = Beginning Postsecondary Students. B&B Variables Major-related variables. We use the B&B to create several measures of average labor market and educational outcomes by undergraduate major. We calculate the median personal income from work for each major in each of the three survey years. All those who responded to income ques- tions are included in these calculations, including those with $0 income. We also determine, for each year, the proportion of individuals in each major whose incomes fall below a threshold: the median net compensation for that year (Social Security Administration 2016). This statistic gives median income for all workers with any income in the United States, regardless of educational attainment, age, or full-time or part-time status, and so is a decent threshold for indicating
728 Sociological Perspectives 60(4)
relatively low wages for college graduates ($16,118 in 1994; $18,277 in 1997; $22,576 in 2003).
We measure the proportion of individuals with each major who reported experiencing an unem-
ployment spell of three months or greater in the first four years after graduation, and the propor-
tion by major who enrolled in graduate school at any point within 10 years following bachelor’s
completion. In addition, we create a “risky major” index through factor analysis (described in the
“Results” section).
Individual-level variables. For the regression analyses we perform using the B&B, we use as a
dependent variable the (log) income in each of the survey years. Graduate school status in each
year is coded categorically: never attended graduate school, attended but did not complete, and
completed a graduate degree. We use as control variables race (black/Latino vs. a white/Asian/
Other reference), gender, age at bachelor’s degree (linear and quadratic), and parental education.
Precollege family income is measured through a dummy for Pell recipient status and a dummy
identifying students who said they were ineligible for need-based aid because their household
income was too high. Prior academic preparation is measured through SAT/ACT score quartiles.
College selectivity is measured through 2004 Barron’s Selectivity Scores (Schmidt 2009), which
assign colleges to one of seven categories (such as “most competitive,” “highly competitive,”
“very competitive,” etc.) on the basis of their acceptance rate, students’ standardized test scores,
and students’ secondary school class rankings. We exclude students who attended a college in the
Barron’s “special” category (which includes institutions such as music conservatories and mari-
time academies), and combine the two lowest competitiveness categories into a single reference
group. Descriptive statistics for this sample appear in Table 2.
BPS Variables
Majors chosen by BPS students are modeled using the major-related variables generated using
the B&B and described above. We measure student SES through a scale including household
income (in deciles), parental education, and familial wealth; this last component is a combination
of two dummy variables indicating whether the students or their family owns their own home,
and whether they have non-home assets greater than $10,000. These are combined into a sum-
mated rating scale (α = .61). Demographic controls include race/ethnicity (black, Latino, and
Asian, with white/other as the reference), gender, age at college entry, marital status, a dummy
indicating responsibility for dependent children, and immigrant generation (foreign-born and
second-generation, with native children of natives as the reference).
Academic preparation is measured through SAT/ACT math and verbal scores (equivalized by
NCES). Only 8 percent of our sample did not take either test (see Table 2). Because these scores are
nonexistent rather than missing, imputation seemed inappropriate. We therefore created a dummy vari-
able equal to 1 if standardized test scores were not available, assigned each of these cases a test score of
400, and interacted the dummy with both math and verbal score variables. Although we understand that
this strategy can bias estimates, our parameter estimates are nearly identical to those obtained using
listwise deletion. Standardized test scores are divided by 10 to ease coefficient interpretation.
Students may switch majors during their time in college, and BPS respondents were asked
about their intended major during freshman year. We classified students as occupying one of four
categories: (1) no change between freshman year and transcript (the reference), (2) changed to a
similar major (i.e., within the natural sciences from biology to chemistry), (3) changed to a very
different major (i.e., from visual arts to business), and (4) undecided during freshman year.
Finally, we incorporate three measures of characteristics of undergraduate institutions. College
selectivity is again measured through 2004 Barron’s scores, and we include college size (the
natural log of enrollment), and the percentage of students receiving federal need-based aid
(divided by 10). Descriptive statistics for this sample also appear in Table 2.Monaghan and Jang 729 Table 2. Descriptive Statistics for the Baccalaureate & Beyond 1993–2003 and Beginning Postsecondary Students Longitudinal Study 2004–2009 Samples. Variables M SD M SD Age first study year 24.99 6.42 19.31 4.30 Female 0.53 0.56 Black 0.06 0.11 Latino 0.05 0.10 Asian 0.04 0.06 First-generation immigrant 0.11 Second-generation immigrant 0.11 Has dependent child 0.04 Married 0.03 Household income first study yeara $63,283 $62,750 Pell recipient 0.18 High-income household 0.20 Parental education: no college attendance 0.32 0.25 Parental education: bachelor’s or more 0.49 0.57 Family wealth > $10,000 0.30 Family homeownership 0.84 SAT equivalized score 1,034.12 196.38 1,062.57 185.90 Took SAT/ACT 0.72 0.92 College selectivity: noncompetitive 0.14 0.19 College selectivity: competitive 0.38 0.44 College selectivity: very competitive 0.28 0.20 College selectivity: highly competitive 0.13 0.12 College selectivity: most competitive 0.07 0.05 N 5,160 6,720 Source. National Center for Education Statistics (2005, 2011). aQuantities are median and interquartile range. Analytic Technique As our dependent variables are continuously distributed, we use ordinary least squares regres- sion. As we have a small number of values for each dependent variable, we use standard errors that are robust to heteroskedasticity. Results Majors, Income, and Graduate School Analysis of the B&B data reveals that many college graduates struggle in their first years out of college, but that after a decade most have attained relative prosperity. In 1994, the median income of B&B respondents was $15,900, in comparison with roughly $60,500 and $43,200 for male and female college graduates in the population generally. By 1997, the cohort’s median income grew to $33,000, and by 2003, it had risen to $51,300 (the national median income for male and female college graduates in 2003 was $61,500 and $44,700, respec- tively).3 Similarly, the proportion with relatively low incomes fell from 63 percent in 1994, to 24 percent in 1997, and finally to only 9 percent in 2003. Overall, 29 percent of the sample experienced a prolonged unemployment spell in the first four years after undergraduate education.
730 Sociological Perspectives 60(4) Looking separately by undergraduate major, we find wide variance in postbaccalaureate expe- riences beginning shortly after graduation. In 1997, only 13 percent of engineering and business majors had incomes below the median net compensation, compared with 37 percent of those who majored in English and 40 percent of those in the natural sciences. These differences could in part be due to differential graduate school attendance, as we can expect graduate students to earn substantially less than nonstudents. Table 3 presents summary statistics of the labor market expe- riences of those who had never enrolled in graduate school in each survey year. Differences in postgraduate experiences remain: in 1997, engineering majors earned 70 percent more than his- tory majors, 31 percent more that natural science majors, and 41 percent more than architecture majors. Overall, those who earn degrees in practical arts fields have higher incomes and are less likely to experience prolonged unemployment. Even pulling out high-flying engineers and com- puter scientists, the average practical arts premium over arts and sciences for bachelor’s holders was 37 percent in 1994, 21 percent in 1997, and 12 percent in 2003. Arts and sciences majors were 26 percent and 49 percent more likely to be low income in 1994 and 1997, respectively, and were 29 percent more likely to experience a prolonged unemployment spell. Hypothesis 1 is supported. Table 3 suggests that majors with higher median incomes have lower associated risk of unem- ployment and of low incomes. At the level of college major, median income and the probability of being low income is −0.95; in 1997, this correlation was −0.83, and by 2003, it had fallen to −0.65. Table 3 also demonstrates that major-based income disparities for those who do not attend graduate school show some stability over time, though the relationship is weakening. Majors’ median income in 1994 is correlated at 0.81 and 0.59 with that measure in 1997 and in 2003, respectively. Table 3 also indicates that those who select majors that net higher salaries for bachelor’s degrees are less likely to ever enroll in graduate programs. At the lower end of the income scale, 60 percent of area, ethnic, and gender studies majors, 66 percent of philosophy majors, and 58 percent of English majors enroll in graduate school by 2003. This is compared with 22 percent of computer scientists, 30 percent of business majors, and 45 percent of engineers. The correlation between majors’ graduate school attendance rate and their rate of prolonged unemployment is 0.59; the correlations between this rate and the proportions low income in 1994 and 1997 are, respectively, 0.62 and 0.56. Figure 1 makes clearer that majors that result in relatively high incomes after four years (for those who have not been to a graduate school) tend to send fewer of their degree holders to gradu- ate school. One should note that practical-arts majors are overrepresented in the bottom-right corner (high-income, low graduate school), as are the most culturally oriented of the arts and sciences in the top-left corner (low income, high graduate school). Hypothesis 2 is clearly supported. Does graduate school completion render majors equally remunerative? To answer this question, we regress individual (log) income in the three survey years on a major-associated probability of graduate school attendance by 2003 (i.e., the quantities in the second to last column of Table 3), interacted with their actual graduate school attendance status in the year in question. Controls were added for race, gender, school selectivity, SAT/ACT score quartile, and age at bachelor’s completion. The results of interest are depicted in the three panels of Figure 2.4 In 1994, only two graduate school statuses were possible, as individuals did not have time to complete a graduate degree. The top panel of Figure 2 shows that individual income one year after undergraduate education is negatively related to their major’s 10-year graduate probability, and that this relationship is similar for those who have and have not enrolled in graduate school (incomes are understandably higher for those who have not). The coefficient for the slope for those who did not attend graduate school is a statistically signifi- cantly different from zero, but that for the difference in slopes is not. The middle panel shows
Table 3. Labor Market and Educational Outcomes for 1993 Bachelor’s Degree Recipients by Undergraduate Major (N = 5,160).
All
respondents Respondents who had never enrolled in graduate school
Ever
Prolonged enrolled in
Median Median Percent low unemployment graduate
income income income spell school Risky
major
Major 2003 1994 1997 2003 1994 1997 2003 1993-1997 1993-2003 index
Arts & sciences
Physical & Natural Sciences 52,326 12,740 34,104 45,717 75% 28% 11% 33% 63% 0.67
Area, Ethnic & Gender Studies 51,300 13,059 17,640 23,229 85% 60% 50% 33% 60% 1.94
Architecture & Related 50,274 15,925 31,752 54,378 69% 5% 17% 37% 36% −0.05
Other Social Sciences 49,248 15,288 31,752 45,657 67% 21% 8% 29% 46% 0.08
Biomedical Sciences 47,709 10,192 29,400 43,092 82% 26% 14% 35% 67% 0.95
Liberal Arts 47,093 16,244 30,576 43,605 58% 20% 13% 25% 47% −0.40
Communication/Journalism 46,950 15,288 30,576 47,709 68% 18% 13% 31% 29% 0.10
Mathematics & Statistics 45,657 16,562 31,164 40,562 66% 15% 0% 23% 67% −0.26
History 45,144 10,511 27,636 41,040 76% 41% 15% 38% 55% 1.19
English 42,066 12,740 29,400 44,118 75% 27% 15% 32% 58% 0.64
Natural Resources & Conservation 42,066 21,658 32,928 42,066 50% 26% 5% 22% 33% −0.61
Philosophy, Religion & Theology 41,040 10,192 23,520 41,040 88% 42% 6% 29% 66% 1.44
Psychology 41,040 15,288 27,636 38,988 74% 31% 14% 35% 59% 0.79
Foreign Language/Literature 40,527 12,103 24,696 43,862 69% 30% 17% 38% 63% 0.63
Visual & Performing Arts 37,449 14,014 28,224 38,988 78% 27% 21% 29% 39% 0.67
Interdisciplinary Studies 35,910 15,288 28,224 35,474 63% 19% 23% 30% 54% −0.12
Arts & sciences average 44,737 14,193 28,702 41,845 71% 27% 15% 31% 53% 0.48
Practical Arts
Engineering 71,820 22,932 47,040 67,716 43% 5% 1% 26% 45% −1.34
Computer Science 67,203 25,480 44,688 61,560 38% 6% 5% 24% 22% −1.54
Engineering Technology 61,560 22,295 44,688 61,560 47% 7% 0% 31% 32% −1.01
Business, Management, & Marketing 56,430 21,658 36,456 53,352 48% 12% 7% 23% 30% −1.04
731
(continued)732
Table 3. (continued)
All
respondents Respondents who had never enrolled in graduate school
Ever
Prolonged enrolled in
Median Median Percent low unemployment graduate
income income income spell school Risky
major
Major 2003 1994 1997 2003 1994 1997 2003 1993-1997 1993-2003 index
Industrial Arts 56,430 19,110 35,868 56,943 59% 21% 6% 15% 21% −0.56
Agriculture & Related 51,300 19,110 36,456 44,631 64% 17% 18% 15% 34% −0.49
Personal, Family & Consumer Services 44,816 14,014 29,753 44,816 75% 19% 12% 23% 31% 0.23
Parks, Recreation & Fitness 43,092 20,384 30,576 43,092 54% 25% 36% 25% 35% −0.41
Security & Protective Services 41,776 19,110 36,456 41,263 58% 19% 6% 24% 27% −0.43
Criminal Justice, Public Admin., & Social Serv. 38,988 15,925 29,400 35,397 63% 23% 12% 34% 48% 0.11
Education 35,910 16,562 27,048 32,473 63% 30% 22% 35% 54% 0.33
Health Professions 51,300 25,480 41,160 51,218 40% 11% 7% 16% 37% −1.53
Practical arts average 51,719 20,172 36,632 49,502 54% 16% 11% 24% 35% −0.64
Practical arts avg., w/o engineering/computer 48,484 19,438 34,954 46,750 57% 18% 13% 24% 35% −0.49
science
Source. National Center for Education Statistics (2005, 2011, 2012).Monaghan and Jang 733 Figure 1. Scatterplot of the relationship between a major’s median income four years after bachelor’s completion (for those who have not attended graduate school) and the major’s 10-year graduate school enrollment rate. Source. National Center for Education Statistics (2005). that in 1997, those who enrolled in graduate-school-oriented majors still had lower incomes on average, and that this result holds even for those who completed a graduate degree. Incomes are higher among those who never attended graduate school, likely reflecting continuous full- time labor market activity. The final panel reveals several points of interest. First, 10 years after completing under- graduate studies, graduate degree holders finally earn more than those who do not enroll in graduate school for most of the range of expected graduate school attendance values. Second, the return to a graduate degree is higher for those whose undergraduate major is associated with high rates of graduate school attendance. At the lower end of major-associated graduate school probability, the return to a graduate degree is quite small. Third, this larger return still only partially mitigates the salary differences between undergraduate degrees of differing graduate school probability. Fourth, for those who themselves do not hold graduate degrees, the penalty for having majored in a field with a high rate of graduate school enrollment is larger. Taken together, this paints an interesting portrait of how graduate school functions in the labor market for those with undergraduate training in different fields. Majors with greater immediate return to the bachelor’s degree tend to be practical arts fields, in which undergraduate education imparts (or signals) skills usable in a specified set of jobs. Bachelor’s degrees in other fields— predominantly the arts and sciences, but also some practical arts majors—have substantially less labor market value even 10 years after graduation, and their holders are, in the years immediately following college, exposed to greater risk of low incomes and unemployment. Those who chose such “risky” majors are in turn more likely to return to school for a higher degree, either because they had planned to do so prior to graduation or in response to disappointing postcollege labor market experiences. Earning graduate degrees permits a partial recuperation of income for those who chose such majors. Thus, Hypothesis 3 is supported.
734 Sociological Perspectives 60(4) Figure 2. Marginal effects of the relationship 10-year graduate school enrollment rate of one’s major on (log) income (one, four, and 10 years after bachelor’s completion), by graduate school enrollment status. Source. National Center for Education Statistics (2005).
Monaghan and Jang 735
Who Chooses “Risky Majors?”
We use these findings regarding the riskiness of various majors to inquire into the determinants
of major choice. To do so, we first constructed a “risky major” index through a factor analysis
of three major-level B&B variables: proportion low income in 1994, proportion low income in
1997, and probability of prolonged unemployment between graduation and the 1997 interview.
These three variables all loaded onto a single factor at 0.57 or greater. Greater values of this
index indicate that the major in question is empirically associated with greater risk of low
incomes and unemployment. The values of this index for each major appear in the rightmost
column of Table 3.
In Table 4, we model the major choices of 2003–2004 first-time freshmen, regressing the
“risky major” index of selected majors on individual and institutional variables. In the first
model, students from higher SES backgrounds are more likely to select riskier majors than lower
SES counterparts, and females are substantially more apt to select them than males. Controlling
for students’ academic preparation and major choice trajectory completely explains the SES rela-
tionship but only mildly reduces the gender coefficient. This evidence presents weak and incon-
sistent support for Hypothesis 4 and strong support for Hypothesis 5.
There is a strong positive relationship between verbal test scores and the selection of riskier
majors; the relationship with math scores is significant and negative (echoing findings of Eide
and Waehrer 1998 and Song et al. 2008). And relative to those with constant major choices, those
who change their major and who enter undecided eventually select majors more associated with
poor early labor market outcomes. This does not mean that they switched to a riskier major but
speaks to the differences in the final field of study.
The next model shows a positive and monotonic relationship between the selectivity of a stu-
dent’s institution and their likelihood of selecting a riskier major. This may indicate that at pres-
tigious schools fewer practical-arts fields are on offer, that the environments encourage in
students a more “academic” and less instrumental understanding of college, or that students
expect major-based income penalties to be compensated for by institutional prestige effects
(Mullen 2010). If the latter is the case, research into the independent effects of selectivity and
major suggests that this confidence is mostly unjustified (Ma and Savas 2014). The negative
relationship between risky major choice and the percentage of one’s classmates who receive
need-based aid could indicate a social influence on major choice: fewer privileged students in
student body leading to a climate in which a pragmatic view of college prevails. There is also a
negative relationship between enrollment size and risky major choice, perhaps because large
institutions tend to have a greater diversity of majors and thus to have more in the practical arts
fields. Hypothesis 6 is strongly supported by this evidence.
To further explore Hypothesis 4, in the final column of Table 4, we interact individual student
SES and institutional selectivity. Because of this interaction,, the coefficient on the SES variable
in this column represents the relationship between SES and risky major choice for the selectivity
reference group—noncompetitive schools. The slope is positive but statistically indistinguish-
able from zero. Coefficients for the interaction terms are negative, indicating that for other selec-
tivity groups, the SES-risky major slopes are either closer to zero (very and highly competitive)
or negative (competitive and most competitive). Main effects of selectivity confirm that students
at more selective schools select riskier majors regardless of SES. But at the most selective
schools, there appears to be a mild negative relationship between SES and risky major choice,
and only within the least competitive schools is the expected positive relationship observed.
Table 5 explores major choice according to two other dimensions: expected short-run
(1997) income for those who do not enroll in graduate school, and probability of graduate
school attendance.5 For income (first two columns), females tend to select majors that earn
their holders nearly $2,500 less per year on average. The SES coefficient is negative and736
Table 4. OLS Regression of Final Major of 2003–2004 First-time Freshmen.
Background Academic Institutional SES/selectivity
Variables controls preparation characteristics interaction
SES 0.059** 0.007 −0.011 0.054
(0.020) (0.021) (0.021) (0.053)
Black −0.048 0.046 0.042 0.046
(0.050) (0.050) (0.052) (0.052)
Latino −0.103 −0.059 −0.022 −0.021
(0.055) (0.055) (0.055) (0.055)
Asian (ref.: white) 0.0016 0.025 0.023 0.017
(0.069) (0.066) (0.066) (0.066)
Female 0.237*** 0.214*** 0.211*** 0.211***
(0.027) (0.028) (0.028) (0.028)
Age −0.010 −0.0023 −0.004 −0.005
(0.006) (0.0078) (0.008) (0.007)
Dependent child −0.154 −0.147 −0.143 −0.128
(0.123) (0.117) (0.119) (0.119)
Married −0.163 −0.093 −0.074 −0.090
(0.157) (0.152) (0.161) (0.161)
First-generation immigrant 0.031 0.079 0.077 0.075
(0.056) (0.056) (0.056) (0.055)
Second-generation immigrant (ref.: third/higher generation) 0.015 0.0088 0.012 0.008
(0.054) (0.051) (0.049) (0.049)
Did not take SAT/ACT −0.069 −0.061 −0.045
(0.102) (0.102) (0.102)
SAT verbal/10 0.018*** 0.0164*** 0.016***
(0.001) (0.001) (0.001)
SAT math/10 −0.005** −0.008*** −0.007***
(0.002) (0.002) (0.001)
Changed to similar major 0.140* 0.140* 0.135*
(0.061) (0.060) (0.060)
(continued)Table 4. (continued)
Background Academic Institutional SES/selectivity
Variables controls preparation characteristics interaction
Changed major category 0.191*** 0.192*** 0.190***
(0.033) (0.033) (0.032)
Undecided major first year 0.188*** 0.149*** 0.149***
(ref.: no major change) (0.033) (0.034) (0.034)
Selectivity: competitive 0.158*** 0.130**
(0.042) (0.045)
Selectivity: very competitive 0.210*** 0.178***
(0.050) (0.053)
Selectivity: highly competitive 0.242*** 0.215***
(0.059) (0.062)
Selectivity: most competitive 0.503*** 0.540***
(ref.: noncompetitive) (0.069) (0.078)
College enrollment (log) −0.097*** −0.098***
(0.013) (0.012)
College % need-based aid/10 −0.029** −0.030**
(0.011) (0.011)
SES × Competitive −0.085
(0.057)
SES × Very competitive −0.036
(0.065)
SES × Highly competitive −0.066
(0.072)
SES × Most competitive −0.223**
(0.086)
Constant −0.144 −1.136*** −0.034 0.003
(0.122) (0.168) (0.223) (0.221)
Observations 6,720 6,720 6,720 6,720
R2 .033 .075 .099 .101
Source. National Center for Education Statistics (2005, 2011, 2012).
Note. Robust standard errors are in parentheses; Dependent variable is chosen field’s risky major index value. OLS = ordinary least squares; SES = socioeconomic status.
*p < .05. **p < .01. ***p < .001
737738
Table 5. OLS Regression of Final Major of 2003–2004 First-time Freshmen.
Predicted short-term income Graduate school attendance rate
Academic prep Academic prep
Background and institutional Background and institutional
Variables controls characteristics controls characteristics
SES −270.3* −8.492 0.007* −0.005
(136.6) (138.4) (0.003) (0.003)
Black 177.7 −164.6 −0.022** −0.004
(332.0) (337.3) (0.007) (0.007)
Latino 475.2 120.7 −0.017* −0.002
(372.6) (363.9) (0.008) (0.008)
Asian (ref.: white) 190.8 −114.5 0.015 0.010
(450.7) (429.2) (0.011) (0.010)
Female −2,468*** −2,245*** 0.033*** 0.033***
(187.5) (185.8) (0.004) (0.004)
Age 32.55 −15.02 −0.001 −0.001
(39.93) (47.54) (0.001) (0.001)
Dependent child 778.1 583.7 −0.015 −0.017
(808.9) (764.1) (0.015) (0.016)
Married 1,148 660.2 0.005 0.019
(975.4) (1,023) (0.019) (0.019)
First-generation immigrant 499.2 139.6 0.018* 0.022**
(378.9) (365.4) (0.008) (0.008)
Second-generation immigrant (ref.: third/higher generation) 164.1 107.2 0.008 0.006
(356.8) (332.2) (0.007) (0.007)
Did not take SAT/ACT 1,143 0.005
(747.2) (0.012)
SAT verbal/10 −86.87*** 0.002***
(11.79) (0.0002)
SAT math/10 82.10*** 0.001**
(12.73) (0.0001)
(continued)Table 5. (continued)
Predicted short-term income Graduate school attendance rate
Academic prep Academic prep
Background and institutional Background and institutional
Variables controls characteristics controls characteristics
Changed to similar major −1,273** −0.002
(426.0) (0.011)
Changed major category −1,756*** 0.013**
(222.5) (0.005)
Undecided major first year −1,718*** 0.009
(ref.: no major change) (218.4) (0.005)
Selectivity: competitive −919.1** 0.012*
(286.5) (0.006)
Selectivity: very competitive −1,045** 0.025***
(339.4) (0.007)
Selectivity: highly competitive −963.4* 0.034***
(402.7) (0.008)
Selectivity: most competitive −2,786*** 0.056***
(ref.: noncompetitive) (454.9) (0.010)
College enrollment (log) 556.5*** −0.013***
(84.48) (0.002)
College % need-based aid/10 234.4** −0.002
(79.82) (0.002)
Constant 33,836*** 31,204*** 0.436*** 0.397***
(752.1) (1,439) (0.015) (0.030)
Observations 6,720 6,720 6,720 6,720
R2 .051 .110 .029 .083
Note. Robust standard errors are in parentheses. Dependent variables are chosen field’s associated 1997 median income for those who had not enrolled in graduate school (first two columns), and chosen
field’s associated graduate school attendance rate. OLS = ordinary least squares; SES = socioeconomic status.
*p < .05. **p < .01. ***p < .001.
739740 Sociological Perspectives 60(4)
statistically significant, and reduced to nearly zero by the inclusion of academic preparation
and institutional characteristics. Students who enrolled at the most selective institutions
choose majors that earn their holders $2,700 less in the short run (compared with those in
nonselective colleges). In the third and fourth columns, the choice of major is presented in
terms of associated graduate school attendance. Higher SES, female, and foreign-born stu-
dents select graduate-school-oriented majors, and black and Latino students select majors that
are less so oriented (relative to reference groups). Again, the SES effect (as well as, here,
racial minority effects) is eliminated by the inclusion of academic and institutional variables.
More academically prepared students—as measured by both math and verbal ability—choose
majors that are associated with graduate school attendance. And there is a monotonic positive
relationship between institutional selectivity and the selection of graduate-school-oriented
majors. The size of an institution is negatively related to the choice of such majors, but the
socioeconomic composition of the student body is not.
Discussion
The choice of an undergraduate major is consequential, and particularly so for those disin-
clined or unable to obtain still further education. We find that the immediate postgraduate
experience varies considerably depending on one’s major. Some majors appear to endow
graduates with sure pathways into steady jobs that pay well, attested to by higher incomes
and low incidence of prolonged unemployment. But graduates with other majors appear to
struggle to find a footing in the years after college. Indeed, even after 10 years, 15 to 20
percent of the graduates of some majors earn less than the median net compensation for all
earners. This may of course be because they attract individuals who are temperamentally
inclined to restlessness, or who have an ideological opposition to joining lucrative sectors of
the economy, or who simply are not very able or clever. But more likely it is because these
majors provide graduates with little more than a stock of knowledge and a piece of paper
certifying that they have completed a given regime of coursework. Providing avenues into
jobs is not what these majors do.
One can respond to or avoid such struggles by proceeding on to a graduate degree, but this is
hardly the route for the risk averse. Graduate degrees are won through time, effort, and sacrifice,
and often also through incurring more debt. In the end, many do not reach their goals: only 57
percent of students complete PhD programs in 10 years (King 2008), and 66 percent of students
complete STEM master’s degrees in four years (Council of Graduate Schools 2013). In addition,
though it may seem restrictive to concentrate on the results of terminal bachelor’s students and
ignore the returns to further degrees, the low rate of bachelor’s completion suggests that this
milestone is itself difficult for many students to attain.
As college-going becomes a near-universal experience, the place of the arts and sciences in
the undergraduate curriculum is increasingly called in question. Nearly all students attend col-
lege at least in part to garner its economic rewards (Pryor et al. 2012). And as college is no longer
the preserve of the affluent, it follows that most students are unlikely to have much in the way of
family resources to fall back on. Given the mounting costs of higher education, these resources
are often drained by the very effort of going to college (Goldrick-Rab 2016). In this context,
selecting one of the arts and sciences disciplines, which offer their students few established links
to the economy, is fraught with risk.
This research resulted in only weak support for a central contention of sociology in this area:
that disadvantaged students are more apt to view college in instrumental terms and therefore to
choose a “useful” (and remunerative) major. The divergence between our results and those
obtained by others may result from use of a different selection or coding of majors, or a different
sample of colleges. But our results are consistent with the well-known mediation of theMonaghan and Jang 741
relationship between social origins and destinations by academic achievement, as well as with
what Bourdieu has labeled “school-mediated reproduction strategies” (Bourdieu 1996). For
Bourdieu, part of the privilege enjoyed by more privileged students is a tendency, derived from
familial background, to find academic subject matter more intrinsically pleasurable—as well as
easier to master. When academically oriented students enter elite schools oriented primarily
toward arts and sciences research, the alignment of habitus and field is near perfect, and the
arrangement seems natural to all concerned.
It also may be that this class-based difference in college orientation has simply attenuated as
rising college costs and an increasingly competitive labor market render the instrumental orienta-
tion universal. Ann L. Mullen (2010), in her study of students at one elite and one nonselective
school, found that only the elite female students displayed anything like the ideal-type liberal arts
orientation toward college: choosing a major with primary reference to its intrinsic interest.
Perhaps in this age of anxiety, college can no longer provide what Michael Oakeshott (2004:28)
described as “the gift of an interval . . . a break in the tyrannical course of irreparable events”—
not even to the affluent.
Conclusion
We investigated two interrelated matters in this study. The first regards which majors place grad-
uates at the greater short-term economic risk, and what the nature of that risk is. In this, we build
on prior research by considering more fully considering the complicating role played by graduate
school enrollment and completion. For those who stop at the bachelor’s degree, we find that
postcollege outcomes vary quite a bit depending on one’s major. Four years after graduation, the
difference between the median incomes of the lowest and highest earning majors was nearly
$30,200 ($38,000 in 2016 dollars). A quarter or more of the graduates of some majors earned less
than the median net compensation for that year, while for others, this was true for less than 10
percent of graduates of other majors. Graduate school attendance rates are correlated at the major
level with indicators of economic insecurity in the years after graduation. This likely indicates
that some individuals avoid a weak labor market for their major by enrolling directly in a gradu-
ate school, and some enroll in response to trying experiences. Net of individual factors, the
graduate school attendance rate of one’s major is negatively related to one’s income. This rela-
tionship only partially dissipates after many years, and mostly for those who enroll in and com-
plete graduate school.
The second matter we address is who selects these risky majors. At the individual level, we
found that higher SES students are more inclined to do so, but this relationship is mediated by
academic preparation, major choice trajectory, and the characteristics of institutions attended.
The propensity of female students to select such majors persists, however, in the presence of
individual- and institutional-level controls. Other individual-level factors, such as race and
nativity, did not display their expected relationships with major choice.
In contrast to prior research, we highlight the role played by institutions in structuring or influ-
encing major choice. The strongest predictors of choosing risky majors were institutional-level
factors: selectivity, size, and the socioeconomic composition of the student body. These findings
suggest that in investigating major choice, researchers should consider how the institutions indi-
viduals attend constrain and structure their choice sets and influence their decisions. We do know
that colleges vary drastically in terms of what they offer and the environment they present their
students. Researchers should move beyond treating “college” as a single undifferentiated cate-
gory and instead recognize its existence as a large set of complex institutions, each with its own
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