Sociology Research

https://doi.org/10.1177/1936724418785411

Journal of Applied Social Science 2018, Vol. 12(2) 145 –163

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Application-Oriented Research

Major Choice and the Wage Differential between Black and White Women

Margaret R. Letterman1, Maryanne T. Clifford1, and Jennifer L. Brown1

Abstract Black workers continue to earn lower salaries than white workers, even among those with comparable levels of education. Previous research has explored the impact that the choice of college major will have on this disparity in earnings. The results of this research suggest that, among men, black bachelor’s degree recipients consistently choose lower paying majors than whites. However, among women, black bachelor’s degree recipients have, in recent years, begun to choose higher paying majors than whites. This recent change in major choice among black women is expected to result in higher starting salaries for black women on average, helping to close the racial earnings gap between black and white women. This paper empirically explores the distributional difference across majors between black and white women in Connecticut and explores the psychological reasons for this shift among black women toward higher paying majors.

Keywords college major, academic achievement, gender roles, college students

Introduction

In January of 2015, the American Association of Colleges and Universities published America’s Unmet Promise: The Imperative for Equity in Higher Education; a document making the case for increasing access to higher education among minority populations (Witham et al. 2015). Such access is expected to increase successful outcomes among minority groups in terms of educa- tional attainment, vocational knowledge, critical thinking skills, and employment opportunities, and, in particular, policies increasing positive educational outcomes among minority groups may indeed help alleviate the persistence of disparate earning across race that has historically plagued the country. However, as monetary returns to higher education can depend heavily on major choice, patterns of choice of major by race and gender may limit the ability of increased educa- tional attainment to substantially reduce the wage gap between whites and blacks, between men and women, and among degree recipient populations of white men (WM), black men (BM),

1Eastern Connecticut State University, Willimantic, CT, USA

Corresponding Author: Maryanne T. Clifford, Department of Economics, Eastern Connecticut State University, Webb Hall, 83 Windham St., Willimantic, CT 06226, USA. Email: [email protected]

785411 JAXXXX10.1177/1936724418785411Journal of Applied Social ScienceLetterman et al. research-article2018

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white women (WW), and black women (BW). To explore this notion further, this paper will build on previous work to explore major choice and expected starting salaries among bachelor’s degree recipients in Connecticut over the course of six academic years.

While perhaps providing a narrow backdrop for this study, Connecticut provides a strong initial foray into this work as its minority population is growing. Specifically, according to the 2010 U.S. Census, Connecticut’s general population expanded at a rate of 4.9 percent between 2000 and 2010. In addition, the minority population is growing as the percentage of blacks in Connecticut has increased from 9.1 percent in 2000 to 10.5 percent in 2010. The notable differ- ence in earnings between black and white workers has drawn more interest as the population of black workers has increased. This difference in earnings is easily visible as between 2007 and 2009, the median household income for a black worker in Connecticut was $43,765, or nearly 60 percent of the typical white worker earnings, with the median white worker earning $72,628 (U.S. Census Bureau 2007, 2009). However, educational attainment is believed to have nar- rowed the earnings gap as, in 2007, the median earnings for black workers with bachelor’s degrees in Connecticut was $48,711, which is 75 percent of the $64,803 median earnings for similarly educated white workers. In addition, bachelor’s degrees awarded to black students by Connecticut institutions of higher education saw a 74 percent increase between 1997 and 2007 as the number of degrees awarded rose from 1,548 in 1997 to 2,698 in 2007, compared with an 11 percent increase (up from 22,187 to 24,601 degrees) in the number of bachelor’s degrees awarded to whites over the same time period (Connecticut Board of Governors for Higher Education 2007).

These figures suggest that an increase in educational attainment among black workers in Connecticut is expected to positively affect earnings and ultimately diminish the earnings gap between black and white workers. However, this paper looks to Connecticut to identify system- atic differences in choice of major among these two groups that are likely to play a role in the persistence of this wage gap. Once initial findings related specifically to patterns in major choice across race and gender among the Connecticut populations are explored, future research will endeavor to expand the scope of this work to a regional or national level.

The remainder of this paper utilizes cross-sectional time series data to explore the relationship between major choice, race, and gender. Building on previous work completed by Free, Brown, and Clifford (2007) for the 2005–2006 academic year, black female bachelor’s degree recipients in Connecticut are found to have chosen relatively higher paying majors than white female bach- elor’s degree recipients as compared with their male counterparts. Differences in expected start- ing salaries (as they are a function of choice of major) are calculated for Connecticut bachelor degree recipients by race and gender across six academic years to determine the persistence of such a finding. Furthermore, the variance of major choice is examined across gender and race to determine the degree to which individual groups choose to concentrate on a small subset of majors. Finally, this paper employs quantile regression analysis to identify the salary sensitivity of each examined group of students in terms of major choice conditional on the distribution of these students across major. Potential policy implications of these findings are then discussed.

Theory

The college major selected by each student can have a profound effect on his or her future earn- ings. The choice of college major can be explained by economists using hedonic wage theory. According to this theory, students consider not only wages but also the nonwage amenities that are likely to result from employment opportunities related to specific college majors when esti- mating the returns to each major. Preferences for or against specific job characteristics may make a particular major field of study, and its corresponding employment positions, more desirable for some students and less desirable for others. Using this economic theory of compensating wage

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differentials or more specifically hedonic wage theory, some college students may be willing to forego choosing a major typically associated with higher wages for one that will likely lead to employment opportunities that are closely related to highly valued nonwage amenities such as the opportunity to help others or a flexible work schedule (Gronberg and Reed 1994; Hersch 1998; Hwang, Mortensen, and Reed 1998).

There is ample evidence to support the hypothesis that women, in general, value nonwage amenities more than their male counterparts (thereby often leading female students to choose majors related to lower paying employment opportunities). However, the economic argument for systematic differences in these preferences across race remains unclear. In the field of psychol- ogy, many researchers have studied differences in academic accomplishment through the devel- opment of theories that take into account variables such as gender, race/ethnicity, socioeconomic status (SES), self-esteem, parental support, and teacher expectations. Research has found that there are gender differences with respect to some variables that could influence preferences for nonwage amenities and, ultimately, the choice of college major and postgraduation occupation. It has also been found that black females score higher than white females on these particular variables. For example, males have been found to score higher on self-esteem measures than females (Chubb and Fertman 1992), and black adolescents score higher on self-esteem measures than white adolescents (Adams, Kuhn, and Rhodes 2006). However, when comparing scores of black males and females, researchers have found no gender differences (Kling et al. 1999).

The Bem Sex Role Inventory (Bem 1974) may provide an explanation for these high levels of self-esteem among black women as, when measuring attributes that have been classified as either masculine or feminine through standardized norming measures, black women have been found to score higher on the Bem masculinity scores than white women, with white women having the overall lowest masculinity scores (De Leon 1993). Other researchers found that female college students who scored higher on the Bem femininity scale were more likely to choose female- dominated majors; those who scored higher on the masculine dimension were more likely to choose male-dominated majors (Murrell, Frieze, and Frost 1991). They also reported that the females who scored highest on the masculinity scale “placed greater importance on material values and job opportunities in career decisions” (Murrell et al. 1991:106). Kling et al. (1999) found a positive correlation with self-esteem and attributes associated with the masculine sex role for males and females. It is not clear whether masculine attributes contribute to higher self- esteem or that conversely, higher self-esteem leads to the acquisition of attributes associated with the male sex role. But both may be implicated in the value placed on nonwage amenities as part of the career-decision-making process for black women.

Holding nonwage amenities constant, economists argue that individuals, regardless of race or gender, may select the major with the highest expected return as measured by the discounted value of net lifetime earnings resulting from specific college majors (Polachek 1978). The dis- counted value of net lifetime earnings is calculated by subtracting the cost of degree completion from the expected returns that will be accrued over a lifetime as a result of said degree.

Under this theory, termed the human capital investment theory, net lifetime earnings will be higher as an individual foresees earning a higher salary and an increased number of years of labor force participation. Likewise, expectations of wage discrimination in particular industries could affect the expected benefit of choosing to pursue a career in those industries. Based on social capital theory, individuals who come from privileged backgrounds may perceive a greater likeli- hood of reward from investment in their own human capital. Whereas, those with more limited means in terms of wealth, social networks, and so on may perceive the lack of social capital to be a hindrance to success in the labor market.

In terms of the cost of degree completion, the lower the perceived cost of degree completion, which is often a function of the individual’s perception of their own abilities, the greater the esti- mate of lifetime earnings will be. Polachek (1978) reports that individuals with higher abilities

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can “acquire a given amount of knowledge with less effort” (Polachek 1978:500); those who have not acquired the preparatory skills to take on a mathematics- or science-based major may not be willing to invest their time or efforts for remediation. In other words, those students who dislike a subject or perceive themselves to be underprepared for certain majors will estimate a higher cost in pursuing these majors than a student that enjoys a particular field of study and/or feels very prepared to pursue a particular major. Finally, other researchers studying students from lower socioeconomic backgrounds found that the students were more likely to enter a field of study if they perceived that the degree had lower costs with a better payoff at completion (Jetten et al. 2008). Therefore, a student who has never felt like he or she was very good at math may avoid mathematically intense fields of study because, even though the lifetime earnings for such fields of study are typically high, the initial time and effort required to successfully complete a mathematically rigorous curriculum may seem prohibitively high to the student, resulting in a low estimate of net benefits for mathematically intense majors. This might suggest that heteroge- neous or varied levels of preparedness (or perceived preparedness) across groups would lead to differences in the way each group values the net benefit of an individual major.

This issue of varied estimates in the net benefits of a major according to race and gender can be further compounded if discount rates utilized by students are dissimilar. Silverman (2003) shows the discount rate, and thus, the value of future earnings varies between men and women, providing motivation for differences in major choice by gender beyond the existence of nonwage amenities. Haushofer, Fehr, and Schunk (2013) illustrate differences in the discount rate depend- ing on gender differences and differences in income levels. Given the historical relationship between race and income level (Thomas and Horton 1992), this could lead black students to discount future earnings differently than their white counterparts potentially leading black stu- dents to systematically choose different majors than white students.

Literature

Several studies have found measurable differences in the distribution of choice in college major across race. For example, St. John et al. (2004) found that black students among Indiana’s public colleges and universities were more likely to have no declared major or to major in social sci- ences, business, and other fields while white students were more concentrated in science and math, health, education, and engineering and technology majors. Weinberger and Joy (2007) noted that among men, black students were more likely than white students to major in education and less likely than white students to major in engineering, business, or computer science. In studying Florida’s public colleges and universities, Pitter et al. (2003) found that black men were more likely than white men to major in low-wage disciplines like protective service and public administration. Finally, Staniec (2004) learned that Asian women and black women are more likely to major in science/engineering/mathematics than white women and less likely to be in humanities/fine arts. However, among all male students, black students were shown to be the only racial group that maintained a significant correlation between race and choice of major once family characteristics and academic accomplishment were controlled for.

In psychology, Gushue and Whitson (2006) found that women who hold less traditional views on sex roles are more likely to have higher career aspirations. The authors found that black women have held dual roles as breadwinner and family caregiver for generations. Black women were also shown to have had a “stronger work orientation, higher work values, a longer history of workforce participation and a more intense commitment to professional goals” (Murrell et al. 1991:108). Black women were found to expect to be working all of their lives (Brannon 1999) as they have historically been more likely to be the heads of households and often the sole support of the family. The high incarceration rate among black men (according to the Bureau of Justice Statistics, one in three black men can expect to go to prison in their lifetime) further supports the

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expectation among black women that future earnings will be central to the well-being of their family. In fact, according to Mechoulan (2011), there is a “positive effect of black male incarcera- tion on black women school attainment and early employment.”

Giele (2008) reported that “among black educated women, there is a more explicit explana- tion—almost what is felt to be a moral imperative—that a woman will use her education in an occupation outside the home for the good of the family and community” (Giele 2008:397). These women are expected to put their focus on their careers; some women who left their professions to remain home with their children full-time were actually criticized for their choice. When white middle class women leave the workforce (or never enter in the first place) to raise children and keep the home, they are merely following the models of their mothers and grandmothers before them. However, most black women have always held dual-roles and have modeled this way of life for their daughters (Giele 2008).

Toldson (2011) reported findings that support the notion that black women disproportionately hold dual roles by examining the number of degrees that were conferred on American black males and females. The most striking differences were that black women earned 270,582 degrees compared with 133,026 degrees earned by black men, at an approximate 2:1 ratio in favor of black women. However, examining the “service” majors (i.e., education, psychology, social sci- ences, and social service) was more interesting as 9 percent of all the degrees earned by black men were in these four areas while black women earned 7.8 percent of these same degrees. The reason that black men, who received one third of all the degrees conferred on black Americans in 2009, are studying in these lower paid “service” majors at a higher rate than black women, how- ever, remains unclear. That is to say, persistent differences in preferences for nonwage amenities across race have yet to be fully explained. This limits the ability of hedonic wage theory in shed- ding light on the differences in the distribution of major choice across race. Human capital invest- ment theory, then, appears to be the most heuristic economic explanation for this phenomenon as it is not unreasonable to expect that individuals of differing race may develop varied expectations regarding the net returns to employment.

In sociological literature, Good, Rattan, and Dweck (2012) report that “stereotype threat” informs women in male-dominated fields that they do not belong and are less valued than their male peers. This negative stereotyping of women’s abilities in mathematical areas actually under- mines female performance in math (Good, Aronson, and Harder 2008). Stereotype threat may actually influence women to switch from a male-dominated major to a more inviting atmosphere with a less “chilly climate” (Walton et al. 2015). However, according to Klinger’s (1977) model, one reaction to stereotype threat includes making an increased effort to disprove such an assess- ment of one’s abilities. Block et al. (2011) called this approach “fending off the stereotype.” Previous research (Edmonson-Bell and Nkomo 1998) indicates that young African American girls are psychologically “armored’ by their mothers to be prepared for the racism and discrimi- nation they will face.

Riegle-Crumb, King, and Moore (2016) examined the differences in males (taking a female- dominated major) and females (taking a male-dominated major) and the likelihood of their switching fields compared with their same-sex peers in other majors. In their investigation, they found that although men were “significantly more likely to switch from a female-dominated major” (Riegle-Crumb et al. 2016:445), there were no differences in changing majors between women in male-dominated fields or women in female-dominated disciplines. These authors also found that women of color were significantly more likely to choose male-dominated fields than white women.

Other significant findings included race/ethnicity differences in overall major switching pat- terns with black men changing majors more often than white men; overall major switching pat- terns in women found that Hispanic women changed majors more often than white women. By investigating social backgrounds, Riegle-Crumb et al. found that males that switched majors

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came from less educated families with less wealth; this correlation was not found with females. Females who switched majors had lower SAT scores than females who stayed in their initial major. For both men and women, those students who changed their majors had lower college grades than students who did not change majors.

This study also found that males who came from families with more education and income were much less likely to choose a female-dominated major. However for women—the more education and family income, the more likely the woman would be enrolled in a male-dominated field. High SAT scores were linked to both male and female students enrolling in male-domi- nated fields.

This paper builds on the current literature by not only exploring the differences in major choice across race and gender but also exploring the persistence of these differences over time and the corresponding gap in the returns to educational attainment that exists across demographic groups based on the majors that graduates are choosing.

Method

Data Sources

This study uses two Connecticut datasets to identify racial differences in the distribution of expected starting salaries for female bachelor’s degree recipients resulting from selected major fields of study. Connecticut data were selected for this study, in part, because of the availability of the population data as opposed to sample data used in most studies. However, several charac- teristics of the state provide a rich backdrop for examining disparities in earned wages across race and gender. In Connecticut, the median household income in 2010 was $65,998, which was above the national average of $49,276 (U.S. Census Bureau 2014). Median household income in Connecticut has also been consistently near the top in the U.S. Census ranking of state incomes in the United States (U.S. Census Bureau 2012–2014). Yet, that wealth is not evenly distributed as, in 2010, the state-level Gini Coefficient, a measure of income distribution across a population, calculated by the U.S. Census bureau found that Connecticut ranks 49th in income equality with only New York and the District of Columbia experiencing greater income inequality across the population (U.S. Census Bureau 2010b). Connecticut also remains among the top five in state U.S. Census rankings of the percentage of the population with bachelor’s degrees (U.S. Census Bureau 2009, 2013).

Major field of study by gender and race. The Degree Completion Database housed by the Connecti- cut Department of Higher Education1 was the primary source of degree completion data used in this study. Through this database, the number of bachelor degrees within the state of Connecticut was acquired for the academic years between 2002 and 2007. The data were composed of infor- mation that colleges and universities provide to the U.S. Department of Education’s Integrated Postsecondary Education Data System and includes, by gender and race, the number of bachelor degrees awarded in the state by academic program (major field of study).

For the 2006–2007 academic year, the state of Connecticut awarded a total of 18,509 degrees. Of these, 7,767 were awarded to men, and 10,742 were awarded to women. Among degrees awarded to women, 828 were awarded to black women, and 7,638 degrees were awarded to white women. Growth in degree recipients in the state of Connecticut is considerable compared with the 1996–1997 academic year where a total of 13,855 degrees were awarded with, 6,200 degrees awarded to men and 7,655 awarded to women. During this time period, 408 were awarded to black women while 6,055 degrees were awarded to white women. The increase in the number of black, female college graduates over this time period was 103 percent while graduation rates

Letterman et al. 151

for white women increased by only 26 percent. Furthermore, during this time period, graduation rates for all women increased by 40 percent.

Average starting salaries. Each year, the National Association of Colleges and Employers (National Association of Colleges and Employers 2003) assembles a Salary Survey compiled of information from college and university career services offices. This Survey estimates the average starting salary offered nationally to bachelor degree recipients by field of study, job function, employer type, and degree level. Salary data for 2007 are provided for 79 different majors. Of these, 65 are offered in Connecticut.

Procedures

Identified in the Connecticut Department of Higher Education Degree Completion Database are more than 100 fields of study (program names). To facilitate the matching of this relatively larger database to the NACE database, the fields of study were sorted into the 65 majors contained within the NACE database according to the degree of commonality across the types of majors listed in each database. To use the available salary data in the most realistic manner possible, sal- ary data from three years prior is matched up with that year’s graduation data to reflect the infor- mation that would have been the most current when the students were selecting their majors. For example, graduates in the year 2003 would have been selecting majors three years prior to gradu- ation when salaries for the year 2000 were available. Thus, salaries from the year 2000 are matched up with major choices in 2003. Table 1 displays the 2009–2010 distribution of bachelor degree recipients across majors with the list of majors sorted from highest paying to lowest.

Using the national starting salary figures provided by the NACE Salary Survey (NACE 1999, 2000, 2001, 2002, 2003, 2004), the expected starting salary in a given year for each demographic group in this study (males, females, black men, black women, white men, and white women) is calculated. This calculation is accomplished by multiplying the percentage of graduates in each major by the major’s average starting salary offered and then totaling across all majors. The ensu- ing result is a weighted average of the starting salary for each group of graduates for each aca- demic year from 1997 to 2007. Estimated starting salaries associated with major choice can be compared between each of the four demographic groups within a given year by calculating the difference in expected starting salary between graduates in each group. This allows for a com- parison of the expected starting salaries of male and female bachelor’s degree recipients, the expected starting salaries of black and white bachelor’s degree recipients, and so on. By utilizing data for all four of the demographic groups (black men, white men, black women, and white women), this paper will consider the possible influences of both gender and race when evaluating major choice differences between black and white women.

Results

Given only the distribution of students across majors, the expected starting salary for Connecticut graduates from 2002 to 2007 is calculated to reveal differing expected starting salaries by race and gender. Specifically, as is shown in Table 2, based on the distribution of graduates across majors, expected starting salaries for male graduates increased between 2002 and 2007 from $30,841.77 to $35,036.21 (row 5 in the table). Likewise, female graduates are expected to have seen an increase in expected starting salaries from $28,936.75 to $32,724.73 (row 6 in the table). Based on major choice, black and white women both experienced an increase in expected starting salaries from 2002 to 2007 with the expected starting salary for black women increasing from $29,399.84 to $33,000.93 and the expected starting salary for white women increasing from $28,674.22 to $32,535.83. However, during this time period, the majors chosen by black women

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Table 1. Percentage of Graduates by Gender and Race in Each Major, Academic Year 2009–2010.

Major Black men

(%) Black

women (%) White

women (%) White

men (%)

Early childhood education 0 0.70 0.54 0 Social work 0.63 3.15 0.61 0.07 Fine arts 5.46 4.78 6.42 5.84 Journalism 0.42 0.35 0.78 1.00 General studies 7.35 8.51 6.81 5.54 Botany/horticulture 0 0 0.06 0.13 Psychology 3.57 16.20 12.74 4.29 English 1.47 3.15 6.15 3.61 Sociology 6.51 6.29 3.29 1.32 Special studies 3.57 5.83 3.91 3.51 Advertising 0 0 0.13 0.05 Public relations 0 0.23 0.96 0.12 Communication and technology studies 4.83 3.96 5.32 3.57 Elementary education 0 0.35 2.52 0.33 Criminal justice 3.15 4.20 2.92 4.41 Social sciences 1.26 1.05 0.83 0.93 Sport, leisure, and exercise sciences 2.31 0.23 0.97 2.12 Biology 4.62 4.78 4.44 3.61 Allied health sciences 1.89 4.55 6.20 3.12 Natural resources 0 0 0.09 0.18 Language and literature 0 0.35 0.78 0.62 Secondary education 0 0.12 0.17 0.12 Art, music, health education 0 0.12 0.75 0.75 Animal science 0 0.23 0.67 0.12 Special education 0 0.12 0.72 0.08 History 2.52 1.17 3.36 4.84 Political science 9.45 4.20 2.90 5.56 Environmental earth science 0 0 0.64 1.17 Hospitality and tourism 0.21 0.23 0.35 0.07 Agricultural education 0.00 0.12 0.03 0.03 International business 0.42 0.12 0.26 0.30 Fashion merchandising 0 0 0 0 Marketing 1.05 1.05 2.26 2.19 Earth sciences 0 0 0.09 0.20 Architectural studies 0.21 0.23 0.13 0.20 Interdisciplinary engineering 0 0 0.01 0.53 Physical sciences 0 0 0.03 0.07 Chemistry 1.05 0.47 0.78 1.05 Business 13.66 11.42 5.74 9.95 Physics 0.42 0 0.18 0.70 Nursing 0.21 3.50 5.46 0.62 Technology management 1.68 0 0.03 0.80 Accounting 3.36 2.68 2.69 4.62 Mathematics 1.26 0.23 1.43 2.12 Construction management 0 0 0.03 0.55 Management information systems 0.42 0.23 0.10 0.73 Economics and finance 9.66 3.03 2.81 10.52

(continued)

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Major Black men

(%) Black

women (%) White

women (%) White

men (%)

Environmental engineering 0 0.12 0.03 0.03 Naval architecture and marine

engineering 0 0 0.05 0.18

Civil engineering 0.63 0.35 0.14 1.05 Computer information systems 0.42 0.35 0.04 0.42 Computer engineering technology 0.63 0.12 0.10 0.58 Biomedical engineering 0.42 0.35 0.14 0.43 Computer science 1.26 0.12 0.31 1.54 Materials science and engineering 0 0 0 0 Mechanical engineering 1.26 0.35 0.19 1.87 Industrial design 0.21 0 0.09 0.15 Electrical engineering 1.47 0 0.01 0.65 Computer engineering 0.63 0 0.03 0.58 Chemical engineering 0.42 0 0.08 0.22 Software engineering 0 0 0.01 0.02 Pharmacy studies 0 0.35 0.69 0.02

Table 1. (continued)

Table 2. Expected Starting Salary (with Three-Year Lag).

2001–2002 2002–2003 2003–2004 2004–2005 2005–2006 2006–2007

Black men 30841.77 33454.85 35771.36 33606.1 33372.82 35036.21 Black women 29399.84 31477.97 33131.62 31876.83 31858.54 33000.93 White men 30978.92 33771.35 35726.67 34029.38 34326 35174.05 White women 28674.22 30999.53 32758.93 31198.97 31692.1 32535.83 Men 31287.76 34073.19 36076.68 34281.65 34398.58 35348.24 Women 28936.75 31200.64 32971.36 31498.4 31806.56 32724.73

consistently resulted in higher expected starting salaries than the majors chosen by white women (see rows 2 and 4 in the table). This same pattern does not hold for male students as, among men, black graduates chose majors for which the expected starting salaries remained consistently below that which was expected for white men (see rows 1 and 3 in the table).

Figure 1 illustrates the difference in expected starting salaries between white men and black men from 2002 to 2007. By contrast, Figure 2 illustrates the difference in expected starting sala- ries between black and white women for the same time period. This difference in expected start- ing salaries gives an indication that major choice varies not only by gender but also across race.2

Indeed, a simple correlation matrix can be used to detect the direction and strength of the relationship between the expected starting salary of a given major and the relative proportion of students within each group choosing that major. As is shown in Table 3, as the average starting salary of a major increases; the proportion of black men in that major increases by a greater amount than the proportion of black women in that major. Likewise, the proportion of white men increases more than the proportion of white women. While these gender differences are to be expected, it can be noted that the strength of the correlation between a major’s average starting salary and the relative proportion of white male bachelor’s degree recipients (as compared with white female bachelor’s degree recipients) within that major (0.2476) is greater than it is for the relative proportion of black male bachelor’s degree recipients (0.2276). This suggests that black

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men may be responding to increases in starting salary to a lesser degree than white men or that black women are responding to increases in starting salary to a greater degree than white women, or both.

In considering this, the correlation between the average starting salary for a major and the dif- ference between the proportion of black women and the proportion of white women in a major is shown in Table 3 to be small but positive. This suggests, again, that black female bachelor’s degree recipients are showing up in higher paying majors in (slightly) greater proportions than white female graduates.

Another indication that major choice varies across race and gender is made obvious by exam- ining the way in which student major choice varies about the mean. This reveals whether specific groups of graduates choose majors whose salaries are tightly distributed about the mean salary for that group or whether the salaries associated with their choice of major choice is more evenly dispersed across a broader choice of majors. To compare the variance of major choice across groups, the F-statistic is calculated for each group as is shown in Table 4.

As is indicated in Table 4, male and female graduates exhibit a statistically significant differ- ence in the way their major choices vary about the mean. Specifically, male graduates are more

Figure 1. Differences in expected starting salaries (white men − black men).

Figure 2. Differences in expected starting salaries (black women − white women).

Letterman et al. 155

varied in the distribution of their major choice than female graduates, suggesting that female graduates choose majors whose salaries are more tightly distributed about the mean salary for women. This result is similar to that which is found when white men are compared with black men as black men choose majors whose salaries are more tightly distributed about their expected starting salary than white men. For women, however, it is white female graduates who choose majors whose salaries are more tightly distributed about their expected starting salary. This again is a reversal from the results found for male graduates.

Finally, the relationship between the average starting salary of a major and major choice by race and gender can be examined using quantile regression analysis. For this analysis, the depen- dent variable is defined as the difference between the proportion of students within one demo- graphic group in a given major and the proportion of students within another demographic group in the same major. For example, the dependent variable BM-BW represents the difference in the proportion of black men in a major and the proportion of black women in the same major. Referring back to Table 1, the value of the dependent variable (BM-BW) for the General Studies major in the 2009–2010 academic year would be 0.0735 − 0.0851 = − 0.0016. In developing a regression equation with this dependent variable and the major specific average starting salary as the independent variable, the estimated coefficient will represent the responsiveness to salary increases of one group relative to the other.

The independent variables for the regression equation are expected to be related to major spe- cific average starting salary. Specifically, though the average starting salary of a major is expected to be a factor in major choice, it is unlikely that this relationship is linear. Instead, it is expected that increases in the average starting salary of a major will increase the proportion of students choosing that major but at a decreasing rate. This yields a quantile regression model as follows:

y = a + b Avg Salary + b Avg Salary1 2 2( ) ( ) . (1)

As there may exist social or cultural conditions beyond the scope of the model provided in Equation (1) affecting the major choice of a particular demographic group, quantile regression methods are utilized so that the results obtained will reveal the responsiveness of each group conditional on the distribution of the proportion of students within that group across all majors. For example, given some simple dependent variable representing the proportion of black men within a major, a regression equation as shown by Equation (1) can be estimated for the 25 per- cent quantile (or the first quartile) to determine the responsiveness of black men to changes in the average starting salary of a major. However, the results will only be for those majors for which the proportion of black men falls below what can be found in the remaining 75 percent of avail- able majors offered. In this way, characteristics of majors within this quantile, other than salary, which implicitly affect the proportion of students within a given demographic group in choosing these majors can be taken into account.

Table 3. Simple Correlations across Variables of Interest.

Average salary (Average salary)2 BM-BW WM-WW BW-WW

Average salary 1.0000 (Average salary)2 .9827 1.0000 BM-BW .2276 .1969 1.0000 WM-WW .2476 .2097 .7808 1.0000 BW-WW .0737 .0559 −.0058 .3908 1.0000

Note. BM = proportion of black men; BW = proportion of black women; WM; proportion of white men; WW = proportion of white women.

156 Journal of Applied Social Science 12(2)

For this paper, four separate dependent variables are examined through quantile regression analysis. The first of these, BM-BW, represents the difference in the proportion of black men and the proportion of black women in a major. The second, WM-WW, represents the difference in the proportion of white men and the proportion of white women in a major. The third, BW-WW, represents the difference in the proportion of black women and the proportion of white women in a major. Finally, the last dependent variable, (BM-BW)–(WM-WW), allows for a comparison of the proportion of black women and the proportion of white women within a given major relative to their male counterparts (or vice versa).

Table 4. Comparisons of Variance across Major Fields of Study by Race and Gender for 2001–2007.

01–02 02–03 03–04 04–05 05–06 06–07

Men vs. women 1.63a 1.60a 1.92a 1.49a 1.41a 1.59a

White men vs. black men 1.15a 1.02a 1.02 1.13a 1.13a 1.05a

White women vs. black women 1.20a 1.07a 1.00 1.02 1.31a 1.02a

aStatistically significant difference between the two groups in terms of the way their major choices vary about the mean.

Table 5. Quantile Regression Results.

1. BM-BW

Explanatory variable 0.10 Quantile 0.25 Quantile 0.50 Quantile 0.75 Quantile 0.90 Quantile Average salary (’000 $)

0.40030*** (0.0658)

0.1696*** (0.0173)

0.0481*** (0.0152)

0.1127*** (0.0276)

0.1279 (0.1110)

(Average salary)2 −0.00378*** (0.000738)

−0.0017*** (0.0002)

−0.000434** (0.000173)

−0.0012*** (0.0003)

−0.0014 (0.0011)

N 317 317 317 317 317 2. WM-WW Explanatory variable 0.10 Quantile 0.25 Quantile 0.50 Quantile 0.75 Quantile 0.90 Quantile Average salary (’000 $)

0.3858*** (0.0970)

0.1784*** (0.0179)

0.1059*** (0.0252)

0.1362*** (0.0367)

0.2275** (0.1021)

(Average salary)2 −0.0036*** (0.0010)

−0.0018*** (0.0002)

−0.0010*** (0.0002)

−0.0013*** (0.0004)

−0.0023** (0.0011)

N 317 317 317 317 317 3. BW-WW Explanatory variable 0.10 Quantile 0.25 Quantile 0.50 Quantile 0.75 Quantile 0.90 Quantile Average salary (’000 $)

0.4247*** (0.0244)

0.1424*** (0.0124)

0.0284*** (0.0048)

0.0024 (0.0231)

−0.1501** (0.0716)

(Average salary)2 −0.0042*** (0.0002)

−0.0014*** (0.0001)

−0.0003*** (0.0001)

−0.0001 (0.0002)

0.0010 (0.0008)

N 317 317 317 317 317 4. (BM-BW)–(WM-WW) Explanatory variable 0.10 Quantile 0.25 Quantile 0.50 Quantile 0.75 Quantile 0.90 Quantile Average salary (’000 $)

0.0710 (0.0472)

0.0170 (0.0366)

−0.0199** (0.0092)

−0.0862*** (0.0223)

−0.2549*** (0.0576)

(Average salary)2 −0.0004 (0.0005)

−0.0001 (0.0004)

0.0002 (0.0001)

0.0008*** (0.0002)

0.0023*** (0.0006)

N 317 317 317 317 317

Note. BM = proportion of black men; BW = proportion of black women; WM; proportion of white men; WW = proportion of white women. *Significance level of 90 percent. **Significance level of 95 percent. ***Significance level of 99 percent.

Letterman et al. 157

As is shown in Table 5, in all but the highest quantile, (where the proportion of black men in a major exceeds the proportion of black women in that major by an amount that is greater than that found in 90 percent of all remaining majors offered), an increase in the average starting sal- ary of a major increases the difference between the proportion of black men and the proportion of black women choosing that major. In particular, these result show that, for those majors where the difference between the proportion of black men and the proportion of black women is very small (smaller than 90 percent of the remaining majors offered), a $1,000 increase in the average starting salary of a major increases the proportion of black men choosing that major by 0.4 more than it increases in the proportion of black women choosing the same major. Furthermore, while this gap increases with average starting salaries, it is also shown to increase at a decreasing rate as is evident by the negative coefficient associated with (Average Salary)2. Similar findings hold across all but the 0.90 quantile and are statistically significant at a 99 percent level of confidence. This suggests that black men are, in most cases, more responsive to salary changes than are black women when it comes to major choice.

The results of the second regression equation, as shown in Table 5, also suggest a greater rela- tive responsiveness to increases in average starting salaries when choosing a major among men. These results compare proportions of white men to proportions to white women within a major and find that, for example, in the 0.10 quantile where the proportions of these populations within a major are very similar, a $1000 increase in the average starting salary of a major increases the proportion of white men choosing that major by 0.3858 more than it increases in the proportion of white women choosing the same major. Furthermore, according to the results shown, while the magnitude of relative salary responsiveness differs by quantile, white men are shown to be sta- tistically significantly more responsive to increasing starting salaries by major than white women regardless of the difference in the proportion of white men and white women in each major.

In examining the responsiveness of black women to changes in average starting salaries as compared with white women, a similar result is found as a positive and statistically significant coefficient is estimated for four out of the five quantiles shown in Table 5. This suggests that, in most cases, black women are more responsive to average salary increases than white women when choosing a major.

To determine whether this responsiveness remains when comparing female graduates to their male counterparts, the fourth regression equation is used. The coefficients in this equation reveal the difference in the relative crowding of women (or men) into a major by race as average starting salary increases. As the results indicate, three of the five quantiles observed show a smaller sal- ary-driven increase in the gap between proportions of men and women in a major among black students than among white students. These results are significant at the 95 percent and 99 percent levels and suggest that, for many majors, black women are responding to increases in average starting salary in a way that is more in-line with their male counterparts than are white women.

Discussion

The study of major choice across race and gender reveals that, not only do female bachelor’s degree recipients consistently choose lower paying majors than male bachelor’s degree recipi- ents, but that there are persistent patterns in major choice by race as well. In particular, the dispa- rate distributions in major choice between black female college graduates and white female college graduates points to a greater proportion of black women in higher paying majors than their white counterparts. This tendency of black women to seek out higher paying majors, which are typically associated with male-dominated careers, is supported by Murrell et al. (1991) and echoes results found by Free et al. (2007).

While estimates of expected starting salaries in this study are based on the distribution of Connecticut’s bachelor degree recipients according to major field of study, many other factors

158 Journal of Applied Social Science 12(2)

including, but not limited to, grade point average (GPA), career-related job and internship experi- ence, and geographic constraints will typically affect starting salaries. Due to data constraints, the scope of this study does not include an examination of these or other variables that can affect the wages of recent college graduates. However, given what appears to be a consistent pattern in major choice across race and gender, the differences in distributions across majors by gender and race and the resulting effects on expected starting salaries provide useful insights regarding the ability of increased educational attainment to reduce the wage differential.

Distributions across majors in 2007 suggest that female bachelor’s degree recipients in Connecticut will earn less than male bachelor’s degree recipients regardless of race. Furthermore, the general clustering of female graduates around particularly low paying majors suggests that one would expect less variance in the salaries earned by women as compared with that earned by male graduates. As these findings are consistent with most studies of gender and choice of col- lege major (Daymont and Andrisani 1984; Jacobs 1995; Polachek 1978; Staniec 2004), hedonic wage theory may prove to be the most useful tool in economics in explaining this apparently strong preference for nonwage amenities.

The most notable result in this study, however, is the fact that, similar to that which was found by Free et al. (2007), a persistent difference in the distribution across majors from 2002 to 2007 generates higher expected average starting salaries for black women than for white women year after year. This result is not consistent with the expected starting salaries among male graduates and, in fact, represents a complete reversal in the education choices made by the black population of graduates in general as compared with their white counterparts. Furthermore, black women are found to be more responsive to increases in average starting salary than white women in three of the five quantiles examined where the proportion of these populations in each major are most similar.

The results of this study reveal the observed decisions regarding major choice, the corre- sponding salary implications of these decisions, and the way in which specific demographic groups differ in their final decisions regarding the pursuit of particular fields of study. What is unobserved, however, is the decision making process individuals engage in when a decision of major choice needs to be made. Economics provides two useful theories, hedonic wage theory and human capital theory, in explaining systematic differences in major choice by gender or by race. However, research in the field of psychology is able to enhance the effectiveness of these theories in examining and attempting to explain systematic differences in major choice among the four specific demographic groups focused on in this study.

As discussed previously with regard to human capital theory, because of the differences in academic achievement across race, as reported by Wiggan (2007), it could be that the cost of completing a highly mathematical course of study is perceived to be higher for black students than for white students, causing a disproportionate concentration of white graduates in these major fields of study. The results of this study support such a theory as black bachelor’s degree recipients are found to choose less mathematically intense, lower paying majors than white grad- uates. Furthermore, one overall finding in psychology has been that girls tend to perform at higher academic levels than boys (Wiggan 2007). This suggests a lower perceived cost of major completion among female students as compared with their male counterparts, potentially result- ing in female students pursuing higher education, in general, and academically rigorous fields of study, in particular, at a higher rate than male students when controlling for preferences for non- wage amenities across the two groups.

This difference in the perceived cost of degree completion among black and white students and among male and female students, as discussed by Wiggan (2007), provides additional sup- port to the notion of human capital theory in explaining patterns in major choice across some of the demographic groups present in this study, though strong preferences for nonwage amenities are expected to confound this study’s ability to detect the degree to which additional levels of

Letterman et al. 159

academic preparedness pushes female students into academically rigorous fields of study. However, an explanation regarding the diverging academic behavior between black and white women requires more information in terms of the difference in preferences for nonwage ameni- ties between these two groups of female students and whether levels of academic preparedness, or perceived preparedness, differ between black and white women, especially as they relate to the perceived preparedness of their male counterparts.

In terms of differences in preferences for nonwage amenities, Murrell et al. (1991) reported that opportunities for career advancement, wages, and social status were the main reasons that black women have cited for choosing a job, whereas white women have reported job enjoyment and being able to use their skills as most important (Murrell et al. 1991). Lease (2004) reported that black students also have more actual knowledge of the world of work, and research has found that black women who choose to enter male-dominated careers were more likely to have had earlier work experience than women who did not make such career choices (Murrell et al. 1991). This experiential gap may prove instrumental in creating these diverging preferences among women for monetary and nonmonetary returns to work.

Further support for the development of differences in preferences may lie in the support struc- ture and the mechanism that students use for major selection. For black college students, “per- ceived family support” has been found to be a significant element in career decisions (Brown 2004:590). Research has also found that the status of both parents had an effect on career aspira- tions and that the mother’s work role had an influence on the “masculinity” of the types of careers that adolescents considered (Brown 2004). Lease (2004) found that black students received more advice about career choices than white students; the black students were also found to have felt that the mentoring they received was more beneficial than white students reported. Lease (2004) suggested that these findings may be related to the evidence that blacks value and maintain a more collectivistic culture and, in turn, may be more receptive to the assistance and advice from mentors, career counselors, and so on. This reliance on familial and social networks among black students and the impact that the mother’s work role has on career aspirations is likely to affect the desirability of careers with large monetary rewards and, by extension, preferences for major fields of study related to these careers.

In considering disparate levels of academic preparedness between black and white women, as compared with their male counterparts, Brannon (1999) reported that there is a slight gender dif- ference in high school graduation rates with more girls graduating than boys. However, the gen- der difference between male and female black students was found to be higher than for male and female white students. This difference has been shown to be even greater in college, with black women graduating at significantly higher rates than black men (Davis et al. 2003). Furthermore, Gushue and Whitson (2006) found that high scores on measures of career decision self-efficacy (CDSE) among young black women is a predictor for a high level of interest in pursuing nontra- ditional careers (male dominated). These scores have been found to be highly correlated with measures of self-esteem (Hughes and Demo 1989). As a result, higher relative levels of self- esteem among black women as compared with white women, as discussed earlier, suggests that black women would be more likely to pursue male-dominated college majors and professional careers as a matter of confidence or perceived preparedness. These findings support human capi- tal theory in explaining the greater proportion of black women, as compared with the proportion of white women, present in mathematically intense, high-paying majors.

As is presented in the sociological literature by Good et al. and by Edmonson-Bell and Nkomo, even though women, in general, encounter a stereotype threat in male-dominated fields, with higher self-esteem, black women are more prepared to disprove such an assessment of their abili- ties. Further research is required to more fully identify and empirically explore the driving eco- nomic, psychological, and socioeconomic forces that lie behind the patterns in choice of major across the demographic groups studied throughout this paper. However, the end result of these

160 Journal of Applied Social Science 12(2)

differences in major choice has significant real-world implications. In particular, while it is expected that an increase in the educational attainment of black students will lead to a reduction in the wage differential, according to the results of this study, this effect, for men, may be damp- ened by differences in major choice that exist between black and white bachelor’s degree recipi- ents. For women, however, this does not appear to be the case as, due to their favorable distribution across college majors, increased educational attainment among black women can be expected to produce a greater financial return than that which would be produced by white women seeking the same level of education. Because of this, increased educational attainment among black women would be expected to be particularly effective in reducing the size of the racial earnings gap.

In terms of policy, poverty among black families and the racial income gap continue to be an issue. Currently, the U.S. Census Bureau 2016 notes that in 2016, 18.2 percent of black men older than 25 have obtained a bachelor’s degree as compared with 23.1 percent of black women. Furthermore, the U.S. Census states that in 2016, 48.3 percent of black women have never mar- ried, 45.1 percent black families are of female head of household, and 41.5 percent of black fami- lies with a female head of household and children younger than 17 live in poverty as compared with 34.3 percent of all black children living in poverty. There are many government programs aimed at reducing poverty in general and for children in particular, but these programs have had limited success. Yet this paper finds that black women graduating from college select higher earning majors on average, which can, in turn, reduce poverty for themselves and their families. Thus, increasing funding to government programs that make a college education more affordable for black women in the form of Pell grants and subsidized students loans may be an important link in reducing poverty for black families as a whole and in narrowing the racial earnings wage gap among women. It is equally important to continue mentoring and encouraging more black students in general (men and women) to attend college as increased educational attainment will work to decrease the racial earnings gap overall. However, until the psychological and sociologi- cal motivations behind the distribution of major choice across race is fully understood, it will be difficult to define policy that will imbue a college diploma with the ability to narrow the racial earnings gap among men with the same magnitude that it is likely to do among women.

The results of this study illustrate choices that are being made by recent college graduates in Connecticut and find that the basic choice of field of study can have implications for the equita- ble distribution of income across the state. However, there are limitations to what this study can provide in terms of exogenous factors leading to these major choices, especially as it relates to college graduates in other states who face a different socioeconomic climate than those students in Connecticut. Future research involving multistate data is required to extrapolate these results to populations beyond Connecticut.

Related Resources

U.S. Department of Education

The U.S. Department of Education provides information regarding congressionally funded educational programs including higher education. Research and Statistics as well as policy and guidance are also provided. For more information, see https://www.ed.gov/programs/landing

U.S. Department of Education National Center for Educational Statistics

The U.S. Department of Education National Center for Educational Statistics provides a wide variety of information and statistics related to postsecondary education. For more information, see https:// nces.ed.gov/surveys/SurveyGroups.asp?group=2

Letterman et al. 161

U.S. Census Bureau American Community Survey

The U.S. Census American Community Survey provides detailed information about the various demographic groups within the United States. This provides a valuable resource for policy makers and urban planners in developing economic and social policy. For more information, see https:// www.census.gov/programs-surveys/acs/

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. Program Name by six-digit Classification of Instructional Program (CIP) Code with Gender and Racial and Ethnic Characteristics for the 1994–1995 School Year and Beyond, Connecticut Department of Higher Education 2010.

2. For ease of interpretation, Figure 2 finds the premium in expected earnings for black women as com- pared with white women. This is in contrast to Figure 1, which finds the premium in expected earnings for white men as compared with black men.

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Author Biographies

Margaret R. Letterman is a professor of psychology at Eastern Connecticut State University. She received her PhD in psychology from Oklahoma State University. Her research interests include multicultural issues in education and acculturation and memory, learning, and teaching methods and styles.

Maryanne T. Clifford is a professor of economics at Eastern Connecticut State University. She received her PhD in economics from the University of Kentucky. Her research interests include international eco- nomics, economic growth, and the economics of education.

Jennifer L. Brown is an associate professor of economics at Eastern Connecticut State University. She received her PhD in economics from the University of California Santa Barbara. Her research interests include environmental economics, energy economics, and the economics of education.