From Major Preferences to Major Choices: Gender and Logics of Major Choice

Abstract

Research shows that college students choose majors for a variety of reasons. Some students are motivated by potential economic returns, others want to take engaging classes, and others still would like opportunities to help people in their jobs. But how do these preferences map onto students’ actual major choices? This question is particularly intriguing in light of gender differences in fields of study, as men and women may take divergent pathways in pursuit of the same outcome. Using data from the Pathways through College Study (N = 2,639), I show that men and women choose very different majors even when they cite the same major preferences—what I call gendered logics of major choice. In addition, I use earnings data from the American Community Survey to assess how these gendered logics of major choice may be associated with broader patterns of earnings inequality. I find that among men and women who have the same major preferences, men’s major choices are tied to significantly higher prospective earnings than women’s major choices. This finding demonstrates that the ways men and women translate their preferences into majors are unequal from an earnings perspective. Implications for research on higher education and gender are discussed.

Keywords

higher education major choice gender

Major choice is one of the most important decisions students make in college. Students in different majors take different classes that expose them to different content and, in many cases, require different amounts of critical thinking (Arum and Roksa 2011). Research also highlights the earnings differentials between individuals with bachelor’s degrees in different fields of study. Depending on the majors they choose, students may have vastly different economic returns throughout their lifetimes (Kim, Tamborini, and Sakamoto 2015). For these reasons, major choice affects students’ lives not only during college but also many years after college completion (Armstrong and Hamilton 2013; Charles and Bradley 2009; DiPrete and Buchmann 2013; Riegle-Crumb et al. 2012).

When choosing a major, students may have different motivations for their decisions and may weigh fields of study using different criteria. Some students have intellectual motivations and are primarily drawn to majors they find personally engaging (Mullen 2014). Others are more motivated by economic returns and seek out majors that will allow them to maximize their earnings and keep their career options open (Davies and Guppy 1997; Quadlin 2017). Still others hope to identify majors that will allow them to make a difference in the world. These preferences are not mutually exclusive; many students hope to find the one major that checks all of these boxes, but each major presumably fulfills some criteria better than others. Precisely how these links operate, however—that is, from students’ preferences to the specific majors they see as fulfilling those preferences—has yet to be examined empirically.

The question of how college students translate their preferences into actual majors is especially intriguing in light of gender differences in fields of study. A long line of research in the social sciences shows that men and women choose different college majors (Alon and DiPrete 2015; Bradley 2000; Charles and Bradley 2002; Correll 2001; England and Li 2006; Jacobs 1996; Morgan, Gelbgiser, and Weeden 2013). This literature demonstrates that gender shapes students’ choice sets, often affecting whether students see majors as open or closed to them. It is a distinct possibility, then, that men and women translate their preferences into major choices differently. Men may believe that certain majors will maximize their economic returns, and women may believe that entirely different majors are better suited for achieving this same goal. This is potentially a key mechanism in the relationship between gender and major choice. Research often suggests that shifting students’ preferences is key to equalizing gender differences in major choice—that women will be more likely to choose engineering if they care about money, for example, or men will be more likely to choose education if they care about helping people. However, if men and women take different pathways even when they have the same preferences for their major, then attempts to change students’ preferences may fare poorly as a solution to equalizing gender gaps in major choice.

This article addresses two interrelated questions: How are college students’ preferences for majors associated with their actual major choices? And to what extent do these links differ for men and women? To answer these questions, I use data from the Pathways through College Study (PtC). The PtC data were collected at three postsecondary institutions that are diverse in terms of size, geography, and public/private status. To my knowledge, this is the only large-scale national survey with extensive data on students’ preferences for their majors. In addition to these more descriptive questions about major choice, I also assess how men’s and women’s major preferences are associated with projected earnings. To do so, I incorporate prospective earnings data from the American Community Survey (ACS), and I also include an analysis of students’ own expected earnings from the PtC data. These analyses examine whether, given the same major preferences, men and women choose majors with divergent potential economic returns. If this is the case, then these gendered logics of major choice may be an important factor that contributes to income inequality among college-educated workers.

Background

What Preferences Do Students Have for Their Majors?

Much research in the social sciences has considered how students choose a major. Prior studies have focused on many preferences students may have, but I focus on three preferences that have received perhaps the most attention in the literature: economic returns, intellectual engagement, and opportunities to help others. These preferences are not mutually exclusive; students may try to find one major that fulfills all these criteria for them. Yet, some students may focus on one or more of these preferences in their major choice—and the tendency to do so may be patterned along sociodemographic lines.

The first factor is economic returns, which some research calls “extrinsic rewards” because these are usually tangible benefits students receive after completing their education (Ma 2009; Marini et al. 1996). As the cost of attending college has increased, and the debts some students incur have risen alongside these costs (Dwyer 2018; Dwyer, McCloud, and Hodson 2012; Houle 2014; Quadlin and Rudel 2015), scholars and members of the public are beginning to pay more attention to the economic returns tied to college majors. Research shows that bachelor’s degrees in different fields of study can yield dramatically different earnings over a lifetime. In fact, the earnings gaps between college graduates in different majors (i.e., within college) are sometimes larger than that between high school and college graduates (i.e., between education; Kim et al. 2015). Degrees in STEM (science, technology, engineering, and mathematics) fields, business, and health are generally tied to higher earnings, and degrees in the humanities and education are tied to lower earnings, with the more heterogeneous social sciences in between (Kim et al. 2015). Students may be aware of these earnings differences between majors and may use them to help guide their major choice (Cebula and Lopes 1982; Davies and Guppy 1997; Quadlin 2017).

In addition to potential earnings as a component of economic returns, students may also consider the employability of a major, or the extent to which a major will allow them to keep their career options open. Employability and projected earnings typically go hand in hand, but they are occasionally at odds with each other. For example, some research shows that degrees in applied fields, such as nursing and education, are associated with good employment prospects and relatively high earnings in the early career but low occupational status growth over time (Roksa and Levey 2010). This pattern implies that nursing and education may be more favorable in terms of employability but less favorable in terms of projected earnings. Some students may be attuned to employability in their major choice, particularly if they have substantial debts and otherwise lack a safety net from family or other sources (Quadlin 2017). But regardless of whether students recognize the distinction between earnings and employability, this is an added component of economic returns that may factor into major choice.

Scholars have also considered the extent to which students prioritize “intrinsic rewards,” or intangible benefits received during or after college. One commonly cited intrinsic reward is intellectual engagement. College is often billed as the best time of one’s life (Armstrong and Hamilton 2013) not only because of the social side of college but also because students can engage with a wide range of academic subjects. Many students take this opportunity to choose a major that interests them on an intellectual level—especially, as some studies show, if students are relatively privileged. Privileged students can afford to weigh subject matter more heavily than economic returns when choosing their major, presumably because they have a safety net that can protect them against poor employment prospects (Goyette and Mullen 2006; Quadlin 2017). Yet, most students consider intellectual engagement on some level, even if it is not the most important factor.

A final factor is opportunities to help others, which is usually categorized with a subset of intrinsic rewards known as “altruistic rewards” (Ma 2009; Marini et al. 1996). This is an important criterion because many students use their major choice as a precursor to their career choice (Roksa and Levey 2010), and helping others is a frequently cited way to achieve meaning in one’s job (Herzog 1982; Lueptow 1980; Marini et al. 1996). Helping others can take many forms, from teaching or helping children to advocating for underserved populations to advising on legal or economic matters. Students may choose different fields of study depending on the extent to which they weigh altruistic rewards in their major choice as well as their preferred method of helping.

In summary, research often assesses the factors students consider when choosing their college major—and, in turn, which students are more and less likely to prioritize these factors. But most research stops here and does not consider how these preferences are associated with the majors students actually choose. This is a key omission because the majors students perceive as having good economic returns, employability, intellectual engagement, and opportunities to help others can vary enormously from student to student. These perceptions may also be patterned by students’ sociodemographic characteristics—especially gender.

Gender and Logics of Major Choice

The question of how students’ preferences are associated with major choice is particularly salient in light of gender. Theories of gender emphasize that expectations about how men and women should behave guide decision making in social life. Role congruity theory, for example, posits that men and women are expected to have different social roles that align with gendered stereotypes. Men are expected to occupy agentic roles, which project power and confidence, whereas women are expected to occupy communal roles, which emphasize teamwork and likeability (Eagly and Karau 2002). Consistent with role congruity theory, studies show that men and women cite different reasons for choosing majors and careers. Women typically place more emphasis on intrinsic rewards, such as helping others and working with people, while men typically place more emphasis on extrinsic rewards, such as economic returns (Konrad et al. 2000; Ma 2009; Zafar 2013). Some research challenges these premises and shows that women today prioritize extrinsic rewards more than they once did, rendering gender differences in extrinsic reward seeking small or nonexistent (Marini et al. 1996). In addition, gender scholars argue that men’s and women’s preferences are constrained by broader demands, such as their need for greater workplace flexibility (Blair-Loy 2003)—a theme I will refer to throughout this article. Yet, all of this is ultimately separate from whether men and women select the same majors given the same stated preferences.

Prior research suggests men’s and women’s major choices are likely to diverge under these circumstances because they have very different choice sets—that is, the set of fields they see as open to and appropriate for them.¹ Data on U.S. bachelor’s recipients show that men are most overrepresented in engineering, physics, computer science, and economics, and women are most overrepresented in nursing, education, psychology, sociology, and English (England and Li 2006). Much research focuses on gender and STEM fields because women are unlikely to choose these majors despite their strong economic returns as well as the concerted efforts that have been made to increase gender diversity in STEM (Morgan et al. 2013; Xie and Shauman 2005). This is not to say women do not consider majoring in engineering, or men do not consider majoring in nursing; research has called attention to people’s experiences as “tokens” in organizations (Kanter 1977) and in college majors in particular (Sax 1996). Yet, the pool of majors students choose from is often shaped by gendered notions about what a “good” or “appropriate” major would be (Cech 2013; Charles and Bradley 2009).

Although little research examines the relationships between major preferences and major choices, some studies provide clues as to how these patterns might unfold (and, further, how these patterns might differ for men and women). These studies focus mostly on STEM majors and careers, and especially the extent to which students associate intrinsic rewards with STEM. Diekman and colleagues (2010) show that women perceive STEM careers as impeding communal goals (e.g., helping others) more so than other careers, and these perceptions partially account for gender differences in STEM interest. Other work finds that girls report greater intention to enroll in STEM if they see these fields as socially relevant, yet there is no such relationship for boys (Kyte and Riegle-Crumb 2017). Taken together, these studies suggest that women may not view STEM majors favorably if they are motivated to help others (which, as discussed earlier, women often cite as a priority).

The present study builds on earlier research to assess how men and women translate their preferences into actual major choices. This type of inquiry is needed to understand how college students draw on gendered choice sets as they begin to pursue their life goals. Prior research suggests that reshaping men’s and women’s preferences is an important priority if we are to close gender gaps in major choice. This study assesses whether this is the case—or if, alternatively, equalizing men’s and women’s preferences is not enough to overcome broader gendered constraints in major choice.

Major Preferences, Gender, and Projected Earnings

In addition to the more descriptive analyses that predict men’s and women’s major choices, I also examine how students’ major preferences are associated with their prospective earnings (mostly using ACS data). These analyses consider whether gendered logics of major choice potentially contribute to income inequality among college graduates. Earnings certainly are not the only outcome that matters for college graduates. College is also understood as a time for students to develop critical thinking skills, make friends, nurture talents and interests, and broaden their worldviews (McCabe 2016; Stevens, Armstrong, and Arum 2008). But because many students and members of the public emphasize the economic value of a college degree, it is important to consider whether students make trade-offs between projected earnings and other aims when choosing a major—and whether these trade-offs are more pronounced among men or women. For example, even if men and women both emphasize engaging classes in their major choice, they may still choose different majors because they have different ideas about what constitutes an engaging class. If the fields men choose under these circumstances are associated with higher earnings than the fields women choose, then women may make greater financial sacrifices in their pursuit of engaging classes. This is a potentially important source of inequality that has been underexamined in prior research.

To assess these dynamics, I use data primarily from the PtC, an extensive multisite data set designed to capture students’ experiences in higher education. The PtC data set includes rich information about students’ majors, as well as students’ preferences for their majors, making this the only large-scale national data set with sufficient information to conduct this study. Additionally, I incorporate prospective earnings by matching students’ majors with earnings data from the ACS. Linking these two data sets allows me to assess how students’ major preferences are associated with the earnings they can reasonably expect to receive.

Data and Methods

Students were first interviewed for the PtC during their first term, in fall 2014 (N = 2,720), and follow-up interviews occurred in the first-year spring, second-year fall, second-year spring, and third-year spring (Grodsky and Muller 2018). Respondents were drawn from three institutions selected because they were diverse in terms of size, geography, and public/private status and because they had developed programs intended to attract and retain STEM majors.² Thus, one purpose of the PtC data set is to assess the efficacy of these programs—but, as noted earlier, the PtC data set is also uniquely situated for research on the dynamics of major choice because it is one of the only national data sets with information on the factors students considered when choosing majors. Respondents were selected using stratified probability samples of first-time first-year students, making the sample representative of the populations of entering students at the three institutions.

Because the PtC data set places special emphasis on STEM majors, students who majored in these fields are well represented, as outlined in Table 1. In their first term, 30 percent of students (and 36 percent of women) were biology/premedicine majors, 28 percent (and 44 percent of men) were engineering majors, and physics/math majors are also overrepresented compared to national estimates.³ For this reason, these data are especially well suited for examining factors that predict entry intro STEM majors. This is not to say the data set is unable to assess majors other than STEM; indeed, many of the factors examined in the data set are related to the selection of non-STEM majors, as discussed in the Results. Yet, the large population of STEM majors is a strength of the PtC data that is unmatched by similar data sets.

Table 1.

Descriptive Statistics.

	Mean (standard deviation) or percent
Variable	Full sample	Men	Women
Initial major
Physics/math	.08	.10***	.06
Biology (including premed)	.30	.24	.36***
Engineering	.28	.44***	.12
Social sciences	.03	.02	.04***
Arts or humanities	.04	.03	.06**
Business	.05	.05	.05
Education	.05	.02	.08***
Health professions	.04	.01	.07***
Other	.12	.08	.16***
Young-adult prospective earnings (log, from ACS)	10.53 (.27)	10.61*** (.21)	10.44 (.29)
Prime-aged prospective earnings (log, from ACS)	11.05 (.24)	11.13*** (.23)	10.98 (.21)
Own expected earnings at age 30 (log, from PtC data)^a	11.01 (.47)	11.06*** (.44)	10.94 (.50)
Student-level characteristics
Female	.50	—	—
Race
White	.63	.64	.62
Asian	.18	.19	.17
Other	.19	.17	.21**
First-generation college student	.44	.39	.50***
Highest math taken (1–5)	3.15 (1.12)	3.30*** (1.07)	3.00 (1.14)
N	2,639	1,327	1,312

Source: Pathways through College Study (PtC); 2014 American Community Survey (ACS).

Note: Significance levels indicated for group with larger mean or percentage (i.e., men compared to women). Math categories are geometry or algebra II, precalculus, statistics, calculus, and multivariate calculus. ACS earnings are assigned as the median earnings among workers who are employed, currently working, in a given age group, and with a bachelor’s degree in the student’s major.

Values are bottom coded at the 5th percentile and top coded at the 95th percentile to minimize skew. Sample sizes are 2,486 (total), 1,280 (men), 1,206 (women).

p < .01. ***p < .001 (two-tailed tests).

Preferences for Major Choice

The main independent variables are students’ preferences for their major choice. In the first-term interview, respondents were asked (emphasis in the original): “How important is each of the following to you when choosing a college major?” Respondents rated six factors on a scale of 1 (not at all important) to 5 (extremely important): (1) the amount of money earned right after college, (2) the amount of money earned over the course of their career, (3) keeping their career options open for now, (4) engaging entry-level classes, (5) engaging advanced classes, and (6) helping other people in their job or career.⁴ I combined the two “money” items and the two “classes” items because they address similar topics and behave similarly in the analyses. As such, the responses for these items range from 2 to 10, and responses for the “career options” and “helping others” items range from 1 to 5.

Table 2 shows descriptive statistics for these major preferences. The means are similar across items, with the lowest point estimate for engaging entry-level classes (3.45) and the highest point estimate for helping others (3.96). These average scores obscure some key gender differences, however. Men have higher means for short-term earnings (p < .001), long-term earnings (p < .001), and the combined earnings item (p < .001). Additionally, women have a higher mean for helping others (p < .001). This pattern is consistent with past research showing men are attracted to majors and careers that offer extrinsic rewards, whereas women are more likely to seek intrinsic or altruistic rewards (Ma 2009; Zafar 2013). Of course, these preferences also reflect other gendered constraints—for example, jobs that are known to have high extrinsic rewards also tend to involve long hours and poor workplace flexibility (Acker 1990; Blair-Loy 2003; Williams, Muller, and Kilanski 2012), so women may place less emphasis on extrinsic rewards because they do not view these workplaces as compatible with their life goals.

Table 2.

Preferences for Major Choice.

	Mean (standard deviation)
Major preference	Full sample	Men	Women
Money right after college	3.56 (.99)	3.64*** (.99)	3.49 (.97)
Money over course of career	3.89 (.96)	3.95*** (.97)	3.82 (.95)
Money—combined	7.45 (1.81)	7.59*** (1.82)	7.31 (1.79)
Keeping career options open	3.89 (.90)	3.86 (.90)	3.91 (.90)
Engaging entry-level classes	3.45 (1.02)	3.45 (1.02)	3.46 (1.03)
Engaging advanced classes	3.89 (.91)	3.89 (.91)	3.89 (.92)
Classes—combined	7.35 (1.68)	7.34 (1.66)	7.35 (1.71)
Helping others in your job	3.96 (1.00)	3.74 (1.03)	4.17*** (.91)
N	2,639	1,327	1,312

Source: Pathways through College Study.

Note: Significance levels indicated for group with larger mean or percentage (i.e., men compared to women). Respondents were asked, ‘‘How important is each of the following to you when choosing a college major?’’ Responses were entered on a scale of 1 (not at all important) to 5 (extremely important).

***

p < .001 (two-tailed tests).

One challenge in using these data is that the causal ordering between major preferences and major choices is not always clear. Prior research contends that students begin with a sense of their priorities and then select majors and careers that align with those priorities (Cebula and Lopes 1982; Davies and Guppy 1997; Diekman et al. 2010; Kyte and Riegle-Crumb 2017), so this is the causal pathway I emphasize. However, it is also possible that students choose a major and retroactively list the motivating factors most consistent with the major they have settled on (for similar discussions, see Nau, Dwyer, and Hodson 2015; Quadlin 2017). Thus, causal ordering cannot be determined with these data from the first term of college, nor is the survey worded in such a way that causal ordering can be inferred. I consider this issue further in the Conclusion.

Another challenge is that we cannot be sure how respondents ranked their preferences or which preference may have “won out” in their major choice. Because respondents were asked to respond to each item on a 1-to-5 scale, and they could assign the same score to some or all of the items, respondents may not have given much thought to differentiating between the six items. A review of the data shows that only 6 percent of respondents assigned the same score to each item, so straight-lining was not widespread. But because there were six items and only a 5-point scale, each respondent had at least one tie between items. Accordingly, I refer to this construct as “preferences” for choosing a major rather than “reasons” or another term that implies a definitive rank. This also has potential implications in light of the gender component of this study: different preferences may win out for men and women even if respondents list the same criteria as important.

College Major

The initial analyses predict students’ majors during the first term of enrollment. Students were asked to indicate their specific major if they had already declared one (e.g., biomedical engineering). If they had not declared a major, students were asked to indicate which broad category their eventual major would most likely fall under (e.g., engineering).⁵ To keep responses consistent between declared and undeclared students, I sorted the specific majors into the categories the PtC provided for students who had not declared a major. This yielded nine categories (descriptions are as listed in the survey): physical sciences (including mathematics), biological/life sciences (including premed), engineering, social sciences (including psychology and history), arts or humanities (including English, literature, dance, theater, and music), business, education, health professions, and other.⁶ The specific majors included under each category are shown in Appendix A in the online supplement. These categories are generally consistent with past studies that sort majors for purposes of predicting earnings (Kim et al. 2015; Ma and Savas 2014), although the STEM categories are unusually detailed in the PtC data set due to its STEM emphasis.

The analyses focus on students’ majors from the first term of college because these reflect students’ initial inclinations given their major preferences. Majors from the first term are also less subject to sorting processes that intensify as students progress through college, such as the “leaky pipeline” out of STEM fields that disproportionately affects women (Bradley 2000; Jacobs 1996). About 40 percent of students switched their major at least once during the fielding period, which is consistent with national estimates (Denice 2018). Most major switches were within the same or similar categories of majors (e.g., from psychology to sociology).

Prospective Earnings

In later analyses, I consider how students’ major preferences are associated with their prospective earnings. Earnings are only one outcome students consider when choosing a major, but scholars and the public have focused recently on the economic value of college in light of rising costs. I assess prospective earnings to gauge whether gendered logics of major choice are potentially related to earnings inequality among college graduates.

Earnings data come from two sources. I initially use data on prospective earnings from the 2014 ACS (this is the base year of the PtC). The ACS is an ideal data set for this analysis because it provides information on earnings and college majors for a large sample of college-educated workers. Because the earnings used in these analyses are derived from students’ major choices, the analyses essentially reveal whether, given the same preferences for their major, men and women choose majors associated with disparate prospective earnings. Of course, there are other reasons one might expect to see gender gaps in earnings, such as the reality that men tend to occupy more prestigious and higher-paying positions within industries and workplaces. I will discuss these possibilities further, but here I focus on the earnings tied to majors to determine whether students choose majors associated with different earnings potential at the median.

For each of the 173 majors in the 2014 ACS, I identified the median income among individuals who were employed and currently working. This yielded a sample of about 500,000 college-educated workers. Then, recognizing that majors may be associated with different earnings trajectories, and students may be differentially attuned to short- and long-term earnings, I calculated median earnings for two age groups: young adults (i.e., ages 30 and below) and prime-aged workers (i.e., ages 25 to 54). If a student had declared a major, I assigned earnings equal to the median income among workers with a degree in that major. If the student had not declared a major, I assigned earnings equal to the median salary among workers in the same category of majors the student expressed interest in. See Appendixes B (young adults) and C (prime-aged workers) in the online supplement for median earnings by major.

In addition, I used data on students’ own expected earnings at age 30, taken from the first PtC survey. I include this analysis to show how students’ major preferences map onto the salaries they expect to earn soon after labor market entry.

Gender and Sociodemographic Controls

I account for several factors associated with major choice, major preferences, and/or earnings. Gender is the primary category of interest. Other controls include race (coded in the data set as white, Asian, and “other”—a broad category because other groups were small, so the PtC combined several groups to maintain student anonymity); an indicator variable for first-generation college attendance, which I use as a proxy for socioeconomic status; and the highest math course taken in high school, which I use as a proxy for academic preparation.⁷

Analytic Strategy

The analyses are presented in two parts. I begin by examining how students’ major preferences are associated with their major choices. I used multinomial logistic regression to determine students’ chances of choosing each of the nine major categories. All four preferences are included in this model, along with sociodemographic controls.⁸ Then, I used Long and Freese’s (2014) -mtable- postestimation command to determine students’ predicted probability of choosing each major, over the range of each major preference. For example, for the “money earned” item, I calculated students’ predicted probability of choosing each of the nine major categories at each level on the “money” scale, from 2 (not at all important) to 10 (extremely important).⁹ I then tested whether students’ predicted probability of choosing a major changed significantly over the range of major preferences. To use the same example, this would tell us whether students who rated money as extremely important were significantly more or less likely to choose a given major (e.g., business) than those who rated money as not at all important. Separate models are shown for men and women to assess whether major preferences are differentially related to major choice by gender. Because I focus on results from postestimation tests, the underlying regressions are not shown in the main text; these models are shown in Table S1 in the online supplement.

I next show how students’ major preferences are associated with prospective earnings and, in turn, whether these relationships differ by gender. To reiterate, because prospective earnings are assigned from the ACS based on students’ majors, these analyses show whether men and women who have similar priorities ultimately choose majors tied to disparate earnings. Prospective earnings are estimated using quantile regressions, which do not assume homoscedasticity or normality, and are often used for models with earnings as an outcome. As before, all four major preferences are included in this model, although the preferences are presented separately for ease of interpretation. These results are shown as figures in the main text, with the underlying regression shown in Table S2 in the online supplement. All analyses are limited to respondents with complete data on the outcomes and all covariates (60 cases missing on the outcome [2 percent], 21 cases missing on other covariates [less than 1 percent]), bringing the final sample size to 2,639.

Results

How Are Major Preferences Associated with Major Choice?

Table 3 shows how students’ major preferences are associated with major choices. The table is separated into four sections, corresponding to the four preferences included in the data: money earned, keeping career options open, engaging classes, and helping others in your job.

Table 3.

Effects of Major Preferences on Major Choices, by Gender.

	Men			Women
	Not at all important	Extremely important	Diff.	Not at all important	Extremely important	Diff.
Money earned
Physics/math	.17	.09	−.08	.02	.07	+.05*
Biology (including premed)	.26	.17	−.09	.35	.35	.00
Engineering	.27	.54	+.27***	.03	.16	+.13***
Social sciences	.01	.01	.00	.08	.03	−.05
Arts or humanities	.08	.01	−.07	.09	.04	−.05
Business	.00	.08	+.08***	.00	.10	+.10***
Education	.13	.00	−.13	.24	.02	−.22**
Health professions	.02	.00	−.02	.04	.07	+.03
Other	.06	.09	+.03	.15	.16	+.01
Keeping career options open
Physics/math	.06	.14	+.08*	.05	.06	+.01
Biology (including premed)	.16	.23	+.07	.33	.41	+.08
Engineering	.57	.45	−.12	.07	.12	+.05
Social sciences	.03	.00	−.03	.07	.04	−.03
Arts or humanities	.02	.02	.00	.03	.07	+.04
Business	.04	.04	.00	.07	.04	−.03
Education	.00	.02	+.02*	.09	.04	−.05
Health professions	.03	.00	−.03	.17	.05	−.12*
Other	.09	.09	.00	.12	.19	+.07
Engaging classes
Physics/math	.07	.13	+.06	.07	.05	−.02
Biology (including premed)	.30	.17	−.13	.41	.38	−.03
Engineering	.31	.54	+.23**	.07	.13	+.06
Social sciences	.01	.01	.00	.02	.06	+.04
Arts or humanities	.01	.03	+.02	.06	.05	−.01
Business	.18	.02	−.16*	.04	.05	+.01
Education	.05	.00	−.05	.11	.03	−.08
Health professions	.01	.01	.00	.06	.07	+.01
Other	.07	.09	+.02	.17	.17	.00
Helping others in your job
Physics/math	.15	.08	−.07	.14	.04	−.10
Biology (including premed)	.04	.38	+.34***	.17	.43	+.26***
Engineering	.62	.36	−.26***	.28	.08	−.20**
Social sciences	.00	.02	+.02*	.01	.06	+.05**
Arts or humanities	.04	.02	−.02	.11	.04	−.07
Business	.04	.03	−.01	.08	.03	−.05
Education	.00	.02	+.02**	.00	.09	+.09***
Health professions	.00	.02	+.02	.02	.09	+.07***
Other	.11	.07	−.04	.21	.14	−.07

Source: Pathways through College Study.

Note: Multinomial logistic regressions; predicted probabilities reported. ‘‘Not at all important’’ is the predicted probability of choosing a major when a factor is rated not at all important; ‘‘Extremely important’’ is the predicted probability of choosing a major when a factor is rated extremely important. These columns may not add to 100 due to rounding. ‘‘Diff.’’ is the difference in predicted probabilities between ‘‘Not at all important’’ and ‘‘Extremely important.’’ The models include all four factors simultaneously as well as controls for race, whether the respondent was a first-generation college student, and highest math course taken in high school (values held at their means in calculating predicted probabilities). Sample sizes are 1,327 (men) and 1,312 (women).

p < .05. **p < .01. ***p < .001 (two-tailed tests; predicted probabilities for ‘‘not at all important’’ and ‘‘extremely important’’ are significantly different).

The top section of Table 3 shows results for “money earned,” separately for men and for women. The column labeled “Not at all important” shows students’ predicted probability of choosing each major category when they indicated earnings were not at all important. The column labeled “Extremely important” shows these predicted probabilities when students indicated earnings were extremely important. Finally, the column labeled “Diff.” shows the difference between the two, with tests for whether these predicted probabilities are significantly different for a given major category.

As an initial illustration, the first row shows that men have a 0.17 probability of majoring in physics/math when earnings are not at all important to them, and they have a 0.09 probability of majoring in physics/math when earnings are extremely important. The difference between the two, –0.08, is not statistically significant. In other words, men’s chances of majoring in physics/math are statistically unchanged regardless of the importance they place on earnings. Two other major categories are significantly different over this range, however. Men’s probabilities of majoring in engineering (difference = +0.27, p < .001) and business (difference = +0.08, p < .001) both increase significantly as they place more emphasis on earnings. Put differently, as men view earnings as increasingly important, they are considerably more likely to major in engineering and, to a lesser extent, business. This pattern suggests men tend to choose these majors when they are seeking a reliable route to economic success.

Turning to women, we see they are more likely to choose three majors as they view earnings as increasingly important: physics/math (difference = +0.05, p < .05), engineering (difference = +0.13, p < .001), and business (difference = +0.10, p < .001). Women’s chances of majoring in these fields, particularly engineering and business, increase substantially as they place more emphasis on earnings. Yet remember that women generally deemphasize earnings when choosing a major (see Table 2), which may help explain why women are underrepresented in engineering despite associating this field with high earnings. Even when women place great emphasis on earnings, other preferences may ultimately win out for them. Women are also much less likely to major in education as they view earnings as increasingly important (difference = −0.22, p < .01). Earnings-motivated women, therefore, may systematically opt out of education majors.

The second section of Table 3 shows results for “keeping career options open.” As men view career options as increasingly important, they are more likely to major in physics/math (difference = +0.08, p < .05). Perhaps surprisingly, men are also more likely to choose education under these circumstances (difference = +0.02, p < .05), although this result is substantively small, and men education majors were uncommon in the PtC sample overall. Turning to results for women, we see that when women place more emphasis on career options, they are less likely to major in health professions (difference = −0.12, p < .05). Although health majors are often billed as pathways to stable and relatively high-paying jobs, this message apparently does not resonate with women, or at least the first-year women in the PtC sample. Perhaps women believe health majors offer a reliable, but ultimately limiting, pathway to employment or that the health field is less flexible than other industries.

The third section shows results for “engaging classes.” As men view engaging classes as increasingly important, they are more likely to major in engineering (difference = +0.23, p < .01) and less likely to major in business (difference = −0.16, p < .05). Women, however, are not significantly more or less likely to choose any major as they place greater emphasis on engaging classes. This does not mean women do not value engaging classes. In fact, men and women are nearly identical in terms of the premium they place on intellectual engagement (see Table 2), but women’s desire for engaging classes does not translate to any particular major. This may be because even when women find subjects interesting, other unmeasured beliefs about those fields prevent them from majoring in them—such as perceptions of engineering and other STEM fields as fostering a “chilly climate” toward women (Herzig 2004).

The bottom section of Table 3 shows results for “helping others.” This item yields many significant associations for both men and women. As men view helping others as increasingly important, they are more likely to major in social sciences (difference = +0.02, p < .05), education (difference = +0.02, p < .01), and especially biology/premedicine, which elicits a 0.34 increase in predicted probabilities between those who see helping others as not at all important versus extremely important (p < .001). Furthermore, men are less likely to major in engineering as they view helping others as increasingly important (difference = −0.26, p < .001). Men often associate an engineering major with high earnings and engaging classes, as noted earlier, so perhaps men see engineering as offering material and intellectual benefits but less in the way of benfits to society. Supplementary analyses suggest this perception varies across different engineering fields. Men in civil, agricultural, and biomedical engineering place more emphasis on helping others, whereas male electrical engineers have the lowest point estimate for this item. Yet, this overall trend implies that the most altrusitically motivated men may not gravitate toward engineering.

Opportunities to help others are particularly salient for women’s major choices. As women perceive helping others as increasingly important, they are more likely to major in biology/premedicine (difference = +0.26, p < .001), social sciences (difference = +0.05, p < .01), education (difference = +0.09, p < .001), and health professions (difference = +0.07, p < .01). All four of these majors are female dominated in the PtC sample (see Table 1), and they tend to be female dominated nationwide (England and Li 2006). This is consistent with the notion that women are particularly likely to be attracted to fields that offer altrustic rewards (see Table 2). In addition, women are less likely to major in engineering as they view helping others as increasingly important (difference = −0.20, p < .01), which is in line with the results for men.

Overall, these data provide insight into the majors men and women associate with several key criteria. Men may major in engineering or business if they wish to maximize their economic returns, and they may opt out of these fields if they are oriented toward engaging classes or helping others. Women often major in biology/premedicine, social sciences, education, or health when they place a premium on helping others. Notably, women are overrepresented in education and health despite low enrollment among earnings- and career-motivated women, respectively. This pattern suggests women may ultimately choose majors they associate with altruistic rewards, even if they know they are sacrificing extrinsic rewards. This type of trade-off is not nearly as common for men as it is for women, thus revealing distinctly gendered logics of major choice.¹⁰

These results raise the question, how might these gendered logics of major choice contribute to inequality? In the next section, I consider earnings projections among men and women who share the same major preferences.

Major Preferences and Prospective Earnings

Figure 1 shows how the four major preferences— money earned, career options, engaging classes, and helping others—are associated with men’s and women’s prospective earnings. The labor market has changed considerably over the past few decades, and earnings for newly minted graduates in some fields are not in line with what previous generations had come to expect (see, e.g., Kalleberg 2011). To account for these structural changes, the two panels in Figure 1 represent prospective earnings in two age groups: young adults (ages 30 and below) and prime-aged workers (ages 25 to 54). Prospective earnings are taken from the 2014 ACS and are assigned equal to the median salary among workers with a bachelor’s degree in the student’s major (see Appendixes B and C in the online supplement).¹¹ Underlying regressions are shown in Table S2 in the online supplement.

Figure 1.

Prospective earnings (from American Community Survey [ACS]) over the range of major preferences, by gender, N = 2,639. (Panel A) Prospective earnings calculated using young-adult college graduates (ages 30 and under). (Panel B) Prospective earnings calculated using prime-aged college graduates (ages 25 to 54).

Figure 1 shows a clear pattern: the majors men choose are associated with significantly higher earnings than the majors women choose, regardless of men’s and women’s major preferences. This is generally consistent whether the outcome is young-adult or prime-aged earnings. Results of earlier analyses showed that men and women choose different majors even when they have the same major preferences. Here, we see that when men and women both prioritize economic returns—or most any of the criteria included in the data set for that matter—men’s major choices are associated with significantly higher earnings than women’s.

One exception emerges in the top-right panel: men and women who indicate helping others is extremely important have statistically indistinguishable prospective young-adult earnings. This appears to happen because men’s prospective earnings decline over the range of the “helping others” item. In other words, as men place increasing emphasis on helping others, they tend to select majors associated with lower prospective young-adult earnings. Women’s prospective earnings, conversely, are relatively flat. Once this outcome is expanded to prime-aged earnings in the bottom panel, men who emphasize helping others have higher prospective earnings than like-minded women, presumably because men’s majors have better earnings trajectories. But despite this exception, the overall pattern in Figure 1 shows that men’s and women’s logics of major choice are unequal from an earnings perspective.

Another notable pattern is that gender gaps in prospective earnings are generally not significant at the lower ends of these items. This is partly a function of small cell sizes at the lower ends of the distributions. In the most extreme case, only 32 students (1 percent) indicated that keeping their career options open was not at all important, thus rendering any gender differences in prospective earnings nonsignificant. But notwithstanding the small cell sizes, many point estimates of prospective earnings are similar for men and women who see these criteria as unimportant. In an exception, men who view engaging classes as relatively unimportant (i.e., a score of 3 on a 2-to-10 scale) have significantly higher prospective earnings than their women counterparts for both young-adult and prime-aged earnings. Supplementary analyses show this is mostly due to the men engineers in this cell who placed little emphasis on engaging classes but had high prospective earnings.

In addition to these main results using ACS data, I conducted two more analyses to examine the prospective earnings outcome further. First, I replicated Figure 1 using sex-specific median earnings—that is, men (women) are assigned the median earnings among men (women) in their major (see Figure S1 in the online supplement). This analysis might provide a better estimate of students’ eventual earnings because it accounts for the fact that men’s earnings tend to be higher than women’s even when they attain bachelor’s degrees in the same field. In general, results with sex-specific prospective earnings are consistent with those shown in Figure 1, although gender gaps in pay are much larger. Note that sex-specific median earnings generally align with non-sex-specific median earnings for young adults, but these two projections are much more divergent for prime-aged workers. This pattern is consistent with research showing the gender pay gap is smallest at labor market entry (Marini and Fan 1997). Yet overall, results are similar regardless of whether prospective earnings are sex specific.

Finally, I recreated these analyses using an alternative outcome: students’ own expected earnings at age 30, measured in the first wave of the PtC data set. Figure 2 shows these results.¹² The sample size is reduced slightly due to missing data on the outcome (n = 2,486), but the shape and patterning of results is similar to the results generated with ACS earnings. It is immediately apparent that both men and women appear to overestimate their earnings. The y-axes in Figure 2 are nearly in line with the y-axes for prime-aged workers—not young-adult workers—in Figure 1. This is a potentially important dynamic, especially considering the observed gender gaps in expected earnings. Women tend to expect lower earnings than men regardless of their major preferences, but women still expect higher earnings than what is realistic (especially if we take sex-specific earnings into account). This indicates women are cognizant of their relative placement in the income distribution but perhaps not their absolute placement. Women recognize they may have to sacrifice pay to receive other benefits, such as greater workplace flexibility, but their expected earnings are still greater than what is likely to materialize. To be fair, men also appear to overestimate their earnings, so their expectations are not accurate, either. But women’s mismatch may be more impactful as they enter the workforce and must reconcile their expectations with the realities of the contemporary labor market.

Figure 2.

Own expected earnings at age 30 (from Pathways through College Study) over the range of major preferences, by gender, n = 2,486.

Discussion

How are students’ major preferences associated with their major choices? How do these patterns differ for men and women? And to what extent might these gendered logics of major choice contribute to earnings inequality? Using data from the PtC, this study builds on prior research by showing how students’ broader aims are filtered through gendered choice sets as they select their majors. This is a unique perspective on major choice that goes beyond preferences for extrinsic and intrinsic rewards to understand how students actually put these preferences into action.

I find that the major preferences students often cite—potential amount of money earned, employability, intellectual engagement, and opportunities to help others—are often associated with different major choices by gender. One source of common ground for men and women is their perceptions of majors that will lead to strong economic returns. Among students who place a premium on earnings, both men and women are more likely to choose majors such as business and engineering. Women also show a modest effect in the physics/math category (as well as a negative association between earnings motivation and education, discussed further later).

This may seem like a potentially important pattern in terms of recruiting women to STEM fields, but there are two main counterpoints to keep in mind. First, men place more emphasis on extrinsic rewards than do women—so even if women associate STEM with high earnings, this is a relatively small component of many women’s major choices. Prior research shows that women deemphasize earnings, at least in part because many high-paying jobs entail long hours and poor flexibility. Second, men frequently cite other reasons for choosing STEM majors that do not necessarily emerge for women. Men are more likely to choose physics/math when they want to keep their career options open, and men are more likely to choose engineering when they want to take engaging classes. These patterns suggest men’s logics of major choice point them toward STEM fields more often than women’s, which echoes prior research on gender and STEM. Men tend to perceive and experience the benefits associated with STEM fields, whereas women tend to perceive and experience the constraints these fields can impose, both in higher education and in the workplace.

In addition, perceptions of altruistic rewards are distinctly related to major choice, particularly for women. Women who prioritize altruism are increasingly likely to major in biology/premedicine, social sciences, education, and health. Notably, all of these majors are female dominated, both in the PtC data and according to national estimates. This congruence between helping others and major choice suggests altruistic rewards are at the forefront of many women’s major choices. This is not a new observation; many scholars have noted women’s tendency to choose majors and careers that will give them opportunities to help other people as well as these fields’ greater receptiveness toward women. But this is a noteworthy pattern because no other criterion, for men or women, aligns with major choice to the same extent as women’s preference for altruistically oriented fields.

Aside from showing how students’ major preferences are associated with their actual major choices, these analyses are useful for understanding to what extent men and women see the various major preferences as in conflict—and which preferences ultimately “win out.”Table 4 provides a visual of these patterns. Among men, engineering is positively associated with money earned and engaging classes but negatively associated with helping others. This is a key pattern because men are strongly overrepresented in engineering, thus suggesting men may be satisfied with majors that are mostly oriented toward extrinsic rewards. Similarly, business for men is positively associated with money earned but negatively associated with engaging classes. These patterns imply that when men make a trade-off in their major choice, they are more likely to prioritize economic returns over other rewards, such as engaging classes or altruism.

Table 4.

Summary of Associations between Major Preferences and Major Choices, by Gender.

Major	Money earned	Keeping career options open	Engaging classes	Helping others in your job
Men
Physics/math		+
Biology (including premed)				+
Engineering	+		+	−
Social sciences				+
Arts or humanities
Business	+		−
Education		+		+
Health professions
Other
Women
Physics/math	+
Biology (including premed)				+
Engineering	+			−
Social sciences				+
Arts or humanities
Business	+
Education	−			+
Health professions		−		+
Other

Source: Pathways through College Study.

Note: This table summarizes results reported in Table 3.

For women, some very different trends emerge. Education is positively associated with helping others but negatively associated with money earned. Similarly, health is positively associated with helping others but negatively associated with career options. The fact that women are overrepresented in education and health, despite these combinations of perceived rewards, underscores the importance women place on altruism. Even though many women perceive education and health as having little to offer in terms of earnings or employability, women are still disproportionately likely to major in these fields—presumably because they value altruism and because these fields are receptive to women and offer workplace flexibility. This also highlights a particular facet of the PtC data: because respondents did not rank their major preferences, but rated each preference on a 1-to-5 scale, the data do not explicitly tell us which major preference “won out.” It appears that extrinsic rewards often win out for men, and altruistic rewards often win out for women, but we cannot know for sure without data on students’ explicit rankings. Future research can take this scale construction into account.

By incorporating data on prospective earnings, I show how these gendered logics of major choice may be implicated in broader patterns of earnings inequality. I find that men’s major choices are tied to significantly higher prospective earnings than women’s major choices, even when men and women cite the same preferences. That is to say, the fields women choose when they want to have the strongest economic returns, the widest opportunities, the most engaging classes, and the most opportunities to help people are lower paying than the fields men choose to meet these criteria. This is a key factor in inequality that has been underexamined in prior research. Of course, earnings are not the only thing that matters for major choice, and women’s majors could very well surpass men’s in terms of life satisfaction, flexibility, or other criteria. It is also important to note that the earnings projections are based on more specific fields than the analyses with the PtC data, and the selection of specific fields within broader fields is likely a source of stratification that is not captured elsewhere in the analyses.

Overall, this study demonstrates the importance of gendered choice sets as a mechanism that constrains college majors. Even if men and women cite the same preferences for their major, the majors they choose are highly divergent, with women’s majors being lower status and lower paying. Prior research on major choice suggests that shifting students’ preferences is key to equalizing gender differences in major choice. The patterns observed here, however, suggest that any interventions designed to change students’ preferences may not have the same outcomes for men and women. For example, an intervention designed to encourage high earnings might have different consequences by gender. Different fields come to mind when men and women think of economic returns, engaging classes, and opportunities to help others. Students are likely to choose majors they believe meet these criteria but that are also aligned with their gendered choice set—thus deepening inequality among college graduates.

Throughout this article, I have posited that the most likely causal pathway is from major preferences to major choices. This is the mechanism assumed in most prior research. However, it is also plausible that students work backward: students might choose a major and then list the motivating factors they see as most consistent with the major they have already chosen. An alternative possibility is that students were not answering for themselves when they answered the questions about major preferences—instead, they may have indicated which criteria should be more and less important for college students broadly defined. This is unlikely because it would have required students to think abstractly, and the PtC survey capitalized you in this question (“How important is each of the following to YOU when choosing a college major?”), but some students might have interpreted the question this way.

A key strength of this study is its use of the PtC data set, which is the only large-scale national survey (to my knowledge) that examines the factors students considered in their major choice. Yet, because respondents were drawn from institutions that emphasize STEM entry and persistence, they differ from the general population of college students. The percentage of students majoring in physics, math, engineering, and several other fields is relatively high, and the percentage majoring in non-STEM fields is relatively low. In a more representative sample, perhaps students would be less likely to choose STEM fields given their various major preferences. Economically motivated students might be especially likely to choose majors other than STEM, as STEM fields are typically perceived as challenging, and most students would choose an easier major if their primary goal was to make money and they were not already inclined toward STEM. Future research can examine these dynamics in a broader sample to understand major choice in the general population.

College students weigh multiple factors when choosing a field of study. In addition to the many perceptible costs and benefits students consider, they are also constrained by gendered conceptions of what a good or appropriate major would be. This study demonstrates that gendered logics of major choice send students on different pathways while also contributing to inequality throughout the life course. Scholars should continue to investigate these gendered logics and the role they play in college experiences and outcomes.

Research Ethics

The research reported in this article was reviewed and approved by the Institutional Review Board at the University of Wisconsin-Madison. All human subjects gave their informed consent to participate in the Pathways through College Study, and adequate steps were taken to protect participants’ confidentiality.

Supplemental Material

4_-_Online_supplement_Clean – Supplemental material for From Major Preferences to Major Choices: Gender and Logics of Major Choice

Supplemental material, 4_-_Online_supplement_Clean for From Major Preferences to Major Choices: Gender and Logics of Major Choice by Natasha Quadlin in Sociology of Education

Footnotes

Acknowledgements

I am grateful to Chandra Muller, Eric Grodsky, Catherine Riegle-Crumb, Brian Powell, Emma Cohen, and members of the Pathways through College Research Network for their thoughtful comments and assistance. Research reported in this article was supported by the Pathways through College Research Network funded by the National Science Foundation under grant numbers DUE 1317196 and DUE 1317206. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.

ORCID iD

Natasha Quadlin

Supplemental Material

Supplemental material is available in the online version of this journal.

Notes

Author Biography

Natasha Quadlin is an assistant professor of sociology at Ohio State University. Her research focuses on social inequality in the contemporary United States, with an emphasis on access and returns to education. Current projects examine perceptions of responsibility for college costs, income inequality among college graduates, and public attitudes toward gender and sexuality.

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