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Major Payoffs: Postcollege Income, Graduate School, and the Choice of “Risky” Undergraduate Majors

David Monaghan1 and Sou Hyun Jang2

AbstractAlthough 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.

Keywordssocioeconomic 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, USA2City 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: [email protected]

688445 SPXXXX10.1177/0731121416688445Sociological PerspectivesMonaghan and Jangresearch-article2017

2 Sociological Perspectives

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 enrollments

Monaghan and Jang 3

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 that


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 Cheung

Monaghan and Jang 5

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.


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 3,340Practical 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

Monaghan and Jang 7

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.


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.

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 4.30Female 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 $62,750Pell 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 1,062.57 185.90Took 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.

Monaghan and Jang 9

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

10

Tab

le 3

. La

bor

Mar

ket

and

Educ

atio

nal O

utco

mes

for

1993

Bac

helo

r’s

Deg

ree

Rec

ipie

nts

by U

nder

grad

uate

Maj

or (

N =

5,1

60).

Maj

or

All

resp

onde

nts

Res

pond

ents

who

had

nev

er e

nrol

led

in g

radu

ate

scho

olEv

er

enro

lled

in

grad

uate

sc

hool

Ris

ky

maj

or

inde

x

Med

ian

inco

me

Med

ian

in

com

ePe

rcen

t lo

w

inco

me

Prol

onge

d un

empl

oym

ent

spel

l

2003

1994

1997

2003

1994

1997

2003

1993

-199

719

93-2

003

Art

s &

sci

ence

s

Phys

ical

& N

atur

al S

cien

ces

52,3

2612

,740

34,1

0445

,717

75%

28%

11%

33%

63%

0.67

A

rea,

Eth

nic

& G

ende

r St

udie

s51

,300

13,0

5917

,640

23,2

2985

%60

%50

%33

%60

%1.

94

Arc

hite

ctur

e &

Rel

ated

50,2

7415

,925

31,7

5254

,378

69%

5%17

%37

%36

%−

0.05

O

ther

Soc

ial S

cien

ces

49,2

4815

,288

31,7

5245

,657

67%

21%

8%29

%46

%0.

08

Biom

edic

al S

cien

ces

47,7

0910

,192

29,4

0043

,092

82%

26%

14%

35%

67%

0.95

Li

bera

l Art

s47

,093

16,2

4430

,576

43,6

0558

%20

%13

%25

%47

%−

0.40

C

omm

unic

atio

n/Jo

urna

lism

46,9

5015

,288

30,5

7647

,709

68%

18%

13%

31%

29%

0.10

M

athe

mat

ics

& S

tatis

tics

45,6

5716

,562

31,1

6440

,562

66%

15%

0%23

%67

%−

0.26

H

isto

ry45

,144

10,5

1127

,636

41,0

4076

%41

%15

%38

%55

%1.

19

Engl

ish

42,0

6612

,740

29,4

0044

,118

75%

27%

15%

32%

58%

0.64

N

atur

al R

esou

rces

& C

onse

rvat

ion

42,0

6621

,658

32,9

2842

,066

50%

26%

5%22

%33

%−

0.61

Ph

iloso

phy,

Rel

igio

n &

The

olog

y41

,040

10,1

9223

,520

41,0

4088

%42

%6%

29%

66%

1.44

Ps

ycho

logy

41,0

4015

,288

27,6

3638

,988

74%

31%

14%

35%

59%

0.79

Fo

reig

n La

ngua

ge/L

itera

ture

40,5

2712

,103

24,6

9643

,862

69%

30%

17%

38%

63%

0.63

V

isua

l & P

erfo

rmin

g A

rts

37,4

4914

,014

28,2

2438

,988

78%

27%

21%

29%

39%

0.67

In

terd

isci

plin

ary

Stud

ies

35,9

1015

,288

28,2

2435

,474

63%

19%

23%

30%

54%

−0.

12

Art

s &

sci

ence

s av

erag

e44

,737

14,1

9328

,702

41,8

4571

%27

%15

%31

%53

%0.

48Pr

actic

al A

rts

En

gine

erin

g71

,820

22,9

3247

,040

67,7

1643

%5%

1%26

%45

%−

1.34

C

ompu

ter

Scie

nce

67,2

0325

,480

44,6

8861

,560

38%

6%5%

24%

22%

−1.

54

Engi

neer

ing

Tec

hnol

ogy

61,5

6022

,295

44,6

8861

,560

47%

7%0%

31%

32%

−1.

01

Busi

ness

, Man

agem

ent,

& M

arke

ting

56,4

3021

,658

36,4

5653

,352

48%

12%

7%23

%30

%−

1.04

(con

tinue

d)

11

Maj

or

All

resp

onde

nts

Res

pond

ents

who

had

nev

er e

nrol

led

in g

radu

ate

scho

olEv

er

enro

lled

in

grad

uate

sc

hool

Ris

ky

maj

or

inde

x

Med

ian

inco

me

Med

ian

in

com

ePe

rcen

t lo

w

inco

me

Prol

onge

d un

empl

oym

ent

spel

l

2003

1994

1997

2003

1994

1997

2003

1993

-199

719

93-2

003

In

dust

rial

Art

s56

,430

19,1

1035

,868

56,9

4359

%21

%6%

15%

21%

−0.

56

Agr

icul

ture

& R

elat

ed51

,300

19,1

1036

,456

44,6

3164

%17

%18

%15

%34

%−

0.49

Pe

rson

al, F

amily

& C

onsu

mer

Ser

vice

s44

,816

14,0

1429

,753

44,8

1675

%19

%12

%23

%31

%0.

23

Park

s, R

ecre

atio

n &

Fitn

ess

43,0

9220

,384

30,5

7643

,092

54%

25%

36%

25%

35%

−0.

41

Secu

rity

& P

rote

ctiv

e Se

rvic

es41

,776

19,1

1036

,456

41,2

6358

%19

%6%

24%

27%

−0.

43

Cri

min

al Ju

stic

e, P

ublic

Adm

in.,

& S

ocia

l Ser

v.38

,988

15,9

2529

,400

35,3

9763

%23

%12

%34

%48

%0.

11

Educ

atio

n35

,910

16,5

6227

,048

32,4

7363

%30

%22

%35

%54

%0.

33

Hea

lth P

rofe

ssio

ns51

,300

25,4

8041

,160

51,2

1840

%11

%7%

16%

37%

−1.

53

Prac

tical

art

s av

erag

e51

,719

20,1

7236

,632

49,5

0254

%16

%11

%24

%35

%−

0.64

Pr

actic

al a

rts

avg.

, w/o

eng

inee

ring

/com

pute

r sc

ienc

e48

,484

19,4

3834

,954

46,7

5057

%18

%13

%24

%35

%−

0.49

Sour

ce. N

atio

nal C

ente

r fo

r Ed

ucat

ion

Stat

istic

s (2

005,

201

1, 2

012)

.

Tab

le 3

. (co

ntin

ued)


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.

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).

Monaghan and Jang 13

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).


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 and

15

Tab

le 4

. O

LS R

egre

ssio

n of

Fin

al M

ajor

of 2

003–

2004

Fir

st-t

ime

Fres

hmen

.

Var

iabl

esBa

ckgr

ound

co

ntro

lsA

cade

mic

pr

epar

atio

nIn

stitu

tiona

l ch

arac

teri

stic

sSE

S/se

lect

ivity

in

tera

ctio

n

SES

0.05

9**

0.00

7−

0.01

10.

054

(0.0

20)

(0.0

21)

(0.0

21)

(0.0

53)

Blac

k−

0.04

80.

046

0.04

20.

046

(0.0

50)

(0.0

50)

(0.0

52)

(0.0

52)

Latin

o−

0.10

3−

0.05

9−

0.02

2−

0.02

1(0

.055

)(0

.055

)(0

.055

)(0

.055

)A

sian

(re

f.: w

hite

)0.

0016

0.02

50.

023

0.01

7(0

.069

)(0

.066

)(0

.066

)(0

.066

)Fe

mal

e0.

237*

**0.

214*

**0.

211*

**0.

211*

**(0

.027

)(0

.028

)(0

.028

)(0

.028

)A

ge−

0.01

0−

0.00

23−

0.00

4−

0.00

5(0

.006

)(0

.007

8)(0

.008

)(0

.007

)D

epen

dent

chi

ld−

0.15

4−

0.14

7−

0.14

3−

0.12

8(0

.123

)(0

.117

)(0

.119

)(0

.119

)M

arri

ed−

0.16

3−

0.09

3−

0.07

4−

0.09

0(0

.157

)(0

.152

)(0

.161

)(0

.161

)Fi

rst-

gene

ratio

n im

mig

rant

0.03

10.

079

0.07

70.

075

(0.0

56)

(0.0

56)

(0.0

56)

(0.0

55)

Seco

nd-g

ener

atio

n im

mig

rant

(re

f.: t

hird

/hig

her

gene

ratio

n)0.

015

0.00

880.

012

0.00

8(0

.054

)(0

.051

)(0

.049

)(0

.049

)D

id n

ot t

ake

SAT

/AC

T−

0.06

9−

0.06

1−

0.04

5

(0.1

02)

(0.1

02)

(0.1

02)

SAT

ver

bal/1

00.

018*

**0.

0164

***

0.01

6***

(0

.001

)(0

.001

)(0

.001

)SA

T m

ath/

10−

0.00

5**

−0.

008*

**−

0.00

7***

(0

.002

)(0

.002

)(0

.001

)C

hang

ed t

o si

mila

r m

ajor

0.14

0*0.

140*

0.13

5*

(0.0

61)

(0.0

60)

(0.0

60)

(con

tinue

d)

16

Var

iabl

esBa

ckgr

ound

co

ntro

lsA

cade

mic

pr

epar

atio

nIn

stitu

tiona

l ch

arac

teri

stic

sSE

S/se

lect

ivity

in

tera

ctio

n

Cha

nged

maj

or c

ateg

ory

0.19

1***

0.19

2***

0.19

0***

(0

.033

)(0

.033

)(0

.032

)U

ndec

ided

maj

or fi

rst

year

(ref

.: no

maj

or c

hang

e)0.

188*

**0.

149*

**0.

149*

**

(0.0

33)

(0.0

34)

(0.0

34)

Sele

ctiv

ity: c

ompe

titiv

e0.

158*

**0.

130*

*

(0.0

42)

(0.0

45)

Sele

ctiv

ity: v

ery

com

petit

ive

0.21

0***

0.17

8***

(0

.050

)(0

.053

)Se

lect

ivity

: hig

hly

com

petit

ive

0.24

2***

0.21

5***

(0

.059

)(0

.062

)Se

lect

ivity

: mos

t co

mpe

titiv

e(r

ef.:

nonc

ompe

titiv

e)0.

503*

**0.

540*

**

(0.0

69)

(0.0

78)

Col

lege

enr

ollm

ent

(log)

−0.

097*

**−

0.09

8***

(0

.013

)(0

.012

)C

olle

ge %

nee

d-ba

sed

aid/

10−

0.02

9**

−0.

030*

*

(0.0

11)

(0.0

11)

SES

× C

ompe

titiv

e−

0.08

5

(0.0

57)

SES

× V

ery

com

petit

ive

−0.

036

(0

.065

)SE

S ×

Hig

hly

com

petit

ive

−0.

066

(0

.072

)SE

S ×

Mos

t co

mpe

titiv

e−

0.22

3**

(0

.086

)C

onst

ant

−0.

144

−1.

136*

**−

0.03

40.

003

(0.1

22)

(0.1

68)

(0.2

23)

(0.2

21)

Obs

erva

tions

6,72

06,

720

6,72

06,

720

R2.0

33.0

75.0

99.1

01

Sour

ce. N

atio

nal C

ente

r fo

r Ed

ucat

ion

Stat

istic

s (2

005,

201

1, 2

012)

.N

ote.

Rob

ust

stan

dard

err

ors

are

in p

aren

thes

es; D

epen

dent

var

iabl

e is

cho

sen

field

’s r

isky

maj

or in

dex

valu

e. O

LS =

ord

inar

y le

ast

squa

res;

SES

= s

ocio

econ

omic

sta

tus.

*p <

.05.

**p

< .0

1. *

**p

< .0

01

Tab

le 4

. (co

ntin

ued)

17

Tab

le 5

. O

LS R

egre

ssio

n of

Fin

al M

ajor

of 2

003–

2004

Fir

st-t

ime

Fres

hmen

.

Var

iabl

es

Pred

icte

d sh

ort-

term

inco

me

Gra

duat

e sc

hool

att

enda

nce

rate

Back

grou

nd

cont

rols

Aca

dem

ic p

rep

and

inst

itutio

nal

char

acte

rist

ics

Back

grou

nd

cont

rols

Aca

dem

ic p

rep

and

inst

itutio

nal

char

acte

rist

ics

SES

−27

0.3*

−8.

492

0.00

7*−

0.00

5(1

36.6

)(1

38.4

)(0

.003

)(0

.003

)Bl

ack

177.

7−

164.

6−

0.02

2**

−0.

004

(332

.0)

(337

.3)

(0.0

07)

(0.0

07)

Latin

o47

5.2

120.

7−

0.01

7*−

0.00

2(3

72.6

)(3

63.9

)(0

.008

)(0

.008

)A

sian

(re

f.: w

hite

)19

0.8

−11

4.5

0.01

50.

010

(450

.7)

(429

.2)

(0.0

11)

(0.0

10)

Fem

ale

−2,

468*

**−

2,24

5***

0.03

3***

0.03

3***

(187

.5)

(185

.8)

(0.0

04)

(0.0

04)

Age

32.5

5−

15.0

2−

0.00

1−

0.00

1(3

9.93

)(4

7.54

)(0

.001

)(0

.001

)D

epen

dent

chi

ld77

8.1

583.

7−

0.01

5−

0.01

7(8

08.9

)(7

64.1

)(0

.015

)(0

.016

)M

arri

ed1,

148

660.

20.

005

0.01

9(9

75.4

)(1

,023

)(0

.019

)(0

.019

)Fi

rst-

gene

ratio

n im

mig

rant

499.

213

9.6

0.01

8*0.

022*

*(3

78.9

)(3

65.4

)(0

.008

)(0

.008

)Se

cond

-gen

erat

ion

imm

igra

nt (

ref.:

thi

rd/h

ighe

r ge

nera

tion)

164.

110

7.2

0.00

80.

006

(356

.8)

(332

.2)

(0.0

07)

(0.0

07)

Did

not

tak

e SA

T/A

CT

1,14

30.

005

(7

47.2

)(0

.012

)SA

T v

erba

l/10

−86

.87*

**0.

002*

**

(11.

79)

(0.0

002)

SAT

mat

h/10

82.1

0***

0.00

1**

(1

2.73

)(0

.000

1)

(con

tinue

d)

18

Var

iabl

es

Pred

icte

d sh

ort-

term

inco

me

Gra

duat

e sc

hool

att

enda

nce

rate

Back

grou

nd

cont

rols

Aca

dem

ic p

rep

and

inst

itutio

nal

char

acte

rist

ics

Back

grou

nd

cont

rols

Aca

dem

ic p

rep

and

inst

itutio

nal

char

acte

rist

ics

Cha

nged

to

sim

ilar

maj

or−

1,27

3**

−0.

002

(4

26.0

)(0

.011

)C

hang

ed m

ajor

cat

egor

y−

1,75

6***

0.01

3**

(2

22.5

)(0

.005

)U

ndec

ided

maj

or fi

rst

year

(ref

.: no

maj

or c

hang

e)−

1,71

8***

0.00

9

(218

.4)

(0.0

05)

Sele

ctiv

ity: c

ompe

titiv

e−

919.

1**

0.01

2*

(286

.5)

(0.0

06)

Sele

ctiv

ity: v

ery

com

petit

ive

−1,

045*

*0.

025*

**

(339

.4)

(0.0

07)

Sele

ctiv

ity: h

ighl

y co

mpe

titiv

e−

963.

4*0.

034*

**

(402

.7)

(0.0

08)

Sele

ctiv

ity: m

ost

com

petit

ive

(ref

.: no

ncom

petit

ive)

−2,

786*

**0.

056*

**

(454

.9)

(0.0

10)

Col

lege

enr

ollm

ent

(log)

556.

5***

−0.

013*

**

(84.

48)

(0.0

02)

Col

lege

% n

eed-

base

d ai

d/10

234.

4**

−0.

002

(7

9.82

)(0

.002

)C

onst

ant

33,8

36**

*31

,204

***

0.43

6***

0.39

7***

(752

.1)

(1,4

39)

(0.0

15)

(0.0

30)

Obs

erva

tions

6,72

06,

720

6,72

06,

720

R2.0

51.1

10.0

29.0

83

Not

e. R

obus

t st

anda

rd e

rror

s ar

e in

par

enth

eses

. Dep

ende

nt v

aria

bles

are

cho

sen

field

’s a

ssoc

iate

d 19

97 m

edia

n in

com

e fo

r th

ose

who

had

not

enr

olle

d in

gra

duat

e sc

hool

(fir

st t

wo

colu

mns

), an

d ch

osen

fie

ld’s

ass

ocia

ted

grad

uate

sch

ool a

tten

danc

e ra

te. O

LS =

ord

inar

y le

ast

squa

res;

SES

= s

ocio

econ

omic

sta

tus.

*p <

.05.

**p

< .0

1. *

**p

< .0

01.

Tab

le 5

. (co

ntin

ued)


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 the


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 structures and processes.


AppendixCrosswalk of Major Categories Available in the B&B:93/03 (MAJCODE1) and in the BPS:04/09 Postsecondary Education Transcript Study (mtmjcgn; Student-school Data File).

Major categories used in text B&B major categories BPS transcript (PETS) major categories

Physical & Natural Sciences Environ Studies; Chemistry; Earth Science; Physics; Physical Sci: All Other

Physical Sciences; Science Technologies/Technicians

Area, Ethnic & Gender Studies American Civilization; Area Studies; African American Studies; Other Ethnic Studies

Area/Ethnic/Cultural/Gender Studies

Architecture & Related Architecture Architecture and related serviceSocial Sciences Anthropology/Archaeology; Economics; Geography;

Sociology; International RelationsSocial Sciences

Biological Sciences Zoology; Botany; Biochemistry; Biol Sci: all Other Biological and Biomedical SciencesLiberal Arts Liberal Studies Liberal Arts/Sci/Gen Studies/HumanitiesCommunication/Journalism Journalism; Communications; Communication Technology Communication, Journalism, Related;

Communication Technology and Support

Mathematics & Statistics Mathematics: All Other Mathematics and StatisticsHistory History HistoryNatural Resources &

ConservationNatural Resources; Forestry Natural Resources and Conservation

English English/American Literature; Writing: Creative/Technical; Letters: All Other; Library/Arch Science

English Language and Literature/Letters

Psychology Psychology; Biopsychology PsychologyPhilosophy, Religious Studies &

TheologyPhilosophy; Religious Studies; Clinical Pastoral Care Philosophy and Religious Studies;

Theology and Religious VocationsForeign Language/Literature Spanish; Foreign Langs: Non-European; Foreign Langs: All

OtherForeign Languages/Literature/Linguistic

Visual & Performing Arts Textiles; Design; Speech/Drama; Film Arts; Music; Art History/Fine Arts; Fine & Performing Arts

Visual and Performing Arts

Interdisciplinary Studies Integrated/Gen Science; Interdisciplinary: All Other Multi/interdisciplinary StudiesEngineering Engineering; Chemical Engineering; Civil Engineering;

Mechanical Engineering; Engineering: All OtherEngineering

Computer Science Computer Programming; Computer and Information Sciences

Computer/Information Science/Support

Engineering Technology Engineering Technology Engineering Technologies/TechniciansIndustrial Arts Military Sciences; Industrial Arts; Commercial art;

Industrial Arts: All Other; Air Transportation; Transportation: Other

Military Technologies; Construction Trades; Mechanic/Repair Technologies/Technician; Precision Production; Transportation and Materials Moving

Business, Management, & Marketing

Accounting; Finance; Business/Mgmt Systems; Management/Business Administration; Secretarial; Business Support; Marketing/Distribution

Business/Management/Marketing/Related

Agriculture & Related Agriculture; Agricultural Science Agriculture/OperationPersonal, Family & Consumer

ServicesHome Econ: All Other; Child Care/Guidance; Vocational

Home Econ.; Basic/Personal SkillsPersonal and Culinary Services; Family/

Consumer Sciences/Human SciencesParks, Recreation & Fitness Leisure Studies Parks/Recreation/Leisure/Fitness StudiesSecurity & Protective Services Protective Services Security and Protective ServicesCriminal Justice, Public

Administration & Social Services

Para-legal (incl Pre-law); Law; Social Work; Public Admin: All Other; City Planning

Legal Professions and Studies; Public Administration/Social Service

Education Early Childhood Ed; Elementary Ed; Secondary Ed; Special Education; Physical Education; Education: Other

Education

Health Professions Dental/Medical Tech; Community/Mental Health; HPER: Non-specialized; Allied Health: Gen & Other; Audiology; Clinical Health Sciences; Medicine; Nursing; Health/Hospital Admin; Public Health; Health Sci/Profession; Dietetics

Health/Related Clinical Sciences

Note. Robust standard errors in parentheses. B&B = Baccalaureate and Beyond; BPS = Beginning Postsecondary Students; HPER = Health/Phys Ed/Recreation.*p < .05. **p < .01. ***p < .001.


Acknowledgments

We would like to thank the editors and reviewers of Sociological Perspectives for their helpful comments on our manuscript, and Paul Attewell for his support of this project.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

1. Ann L. Mullen (2010) discusses lower socioeconomic status students at Yale who prefer “useful” skills-oriented majors but find few on offer.

2. We excluded respondents for whom college selectivity information was unavailable, or the National Center for Education Statistics (NCES) sampling weight was equal to 0. Per NCES requirements, we round all sample sizes to the nearest 10.

3. All figures are in constant 2004 dollars. Median incomes for all college graduates are derived from the work of Snyder, de Brey, and Dillow (2014; Table 502.20, available at https://nces.ed.gov/programs/digest/d14/tables/dt14_502.20.asp).

4. Full regression results are not presented because of space considerations but are available from the authors on request.

5. This income measure—major-specific medians—is here normally distributed (skewness = 0.46; kur-tosis = 3.37) and modeled with ordinary least squares.

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Author Biographies

David Monaghan earned his PhD at the Graduate Center, CUNY in 2015 and is currently a Senior Researcher at the Wisconsin HOPE Lab, University of Wisconsin-Madison. He studies the intersection between higher education and stratification, with a focus on open-access institutions and non-traditional college students. His work has been published in Sociology of Education, Social Science Research, Research in Higher Education, Educational Evaluation and Policy Analysis, and Sociological Focus, and his book ‘Data Mining in the Social Sciences: An Introduction’ was published by UC Press.

Sou Hyun Jang is a doctoral candidate at the Graduate Center, CUNY. Her research interests include inter-national migration, Asian Americans, and medical transnationalism. Her doctoral dissertation investigates how the US healthcare system and transnational ties are related to Korean immigrants’ medical tourism to their homeland. Her most recent publication, accepted in Social Science and Medicine, is about Korean immigrants’ barriers to the US healthcare and their coping strategies. Her other articles mainly focused on Asian Americans, and they were published in Sociology of Religion and Ethnic and Racial Studies.

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