EVOLUTION OF GENDER DIFFERENCES IN POST‐SECONDARY HUMAN CAPITAL INVESTMENTS: COLLEGE MAJORS

• Author: MATTHEW WISWALL,AHU GEMICI
• Published date: 01 February 2014
• DOI: http://doi.org/10.1111/iere.12040
• Date: 01 February 2014

INTERNATIONAL ECONOMIC REVIEW

Vol. 55, No. 1, February 2014

EVOLUTION OF GENDER DIFFERENCES IN POST-SECONDARY HUMAN

CAPITAL INVESTMENTS: COLLEGE MAJORS∗

BYAHU GEMICI AND MATTHEW WISWALL1

New York University, U.S.A; Arizona State University, U.S.A

Although women in the United States now complete more college degrees than men, the distribution of college

majors among college graduates remains unequal, with women about two-thirds as likely as men to major in business or

science. We develop and estimate a dynamic, overlapping generations model of human capital investments and labor

supply. We allow for specific college major choices, instead of aggregating these choices to the education level. Results

show that changes in skill prices, higher schooling costs, and gender-specific changes in home value were each important

to the long-term trends.

1. INTRODUCTION

One of the starkest changes in developed economies over the past several decades has been

the increase in women’s educational attainment. In the United States, the proportion of women

obtaining a college degree has increased more than fourfold, from about 8% for the cohorts

born in the 1920s (graduating from college in the 1940s) to about 35% for cohorts born in the

1960s (graduating from college in the 1980s). The rapid rise in college attainment for women

has reached the point where women are now more likely than men to graduate from college.2

Less widely known is that accompanying this change in the extensive margin of college

attendance and graduation, there were also substantial changes in the intensive margin of

college major choice. For the cohort born in 1920, women who graduated from college obtained

about 84% of their degrees in the humanities, social sciences, or teaching fields, and only 11%

in science, mathematics, or engineering and 5% in business or economics. In contrast, college

educated men born in the same year had around 41% of their degrees in science, mathematics, or

engineering, and 27% in business. Forty years later, for the cohort born in 1960, the proportion

of women earning degrees in science fields nearly doubled to about 20% and the proportion in

business increased fourfold to 25%.3

As Figure 1 shows these changes have resulted in an increase in the female–male ratio of

the proportion of degrees in science and business from the 1920s to 1960s birth cohorts. But

unlike the female–male ratio in college attainment, the gender gap in college major composition

is still far less than parity for these recent cohorts, with women about two thirds as likely as

men to earn a degree in a science or business field. Incorporating this information on college

major choice, we then have a more nuanced picture of the gender differences in educational

attainment: Although women have reached parity with men in rates of college graduation, there

remains a substantial genderdifference in college major choices.

∗Manuscript received June 2011; revised September 2012.

1We thank Jennifer Sallman for excellent research assistance. We thank three anonymous referees and the editor,

Holger Sieg, for helpful comments. All errors are our own.

Please address correspondence to: Ahu Gemici, Department of Economics, NYU, 19 W 4th Street, New York, NY

10012. Phone: 212 998-8958. E-mail: ahu.gemici@gmail.com

2Calculated from Census and Current Population Survey (CPS) data, discussed later. See Figure 3. As Goldin et al.

(2006a) point out, this more recent trend represents a “homecoming” of women to college as the earlier cohorts of

women who graduated from college in the 1900–1930s (born approximately in the 1880–1910s), actually attended

college at the same rate as men.

3See Figures 1 and 2.

23

C

(2014) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social

and Economic Research Association

24 GEMICI AND WISWALL

0.2 0.4 0.6 0.8 1

Ratio: Female to Male

1920 1930 1940 1950 1960 1970

Cohort

Nonhumanities Degrees All College Degress

Gender Parity (Ratio = 1)

SOURCE: 1993 and 2003 NSCG data.

FIGURE 1

FIELD OF STUDY COMPOSITION OF BACHELOR DEGREES BY YEAR OF BIRTH (MEN)

To understand the evolution of these educational choices, this article develops and estimates

a dynamic overlapping generations model of human capital investment and labor supply. Our

main departure from the previous literature is the way we measure human capital, making

a distinction between college degrees with different majors. We define human capital skill

classes by schooling years and degree, including specific college fields of study, instead of by

schooling years only (as in, e.g., Heckman et al., 1998, or Heathcote et al., 2010),or years of

schooling combined with white, blue, and pink collar occupation categories (as in Lee, 2005,

and Lee and Wolpin, 2006, 2009). Our model explicitly incorporates college major selection as

a distinct choice and allows for heterogeneity in major-specific skills and tastes. Our multiple

generations model allows for nonstationary college major-specific rental rates, allowing the

returns to science degrees relative to humanities degrees to vary over time.

Due to data limitations, most notably that the CPS and Decennial Census do not record

college major information, economists studying long-term trends in human capital investments

in the United States typically use years of completed schooling as their measure of human

capital. For the college educated population, years of schooling is a substantially incomplete

measure of their human capital, as the various college majors chosen by college graduates

represent substantial investments in specific human capital, as suggested by the large average

earnings differences between individuals with different majors (compare the average earnings

of an individual with a degree in the humanities vs. one with a degree in engineering). To

overcome the lack of long-term time-series data on college majors, we turn to auxiliary data

from 1993 and 2003 National Survey of College Graduates (NSCG). With the retrospective

questions on college majors, the NSCG data allow us to reconstruct the date of completion

and specific major of the college degrees earned for a large sample of U.S. residents born from

the 1920s to the 1960s. This data set offers the most extensive historical coverage of trends in

college major composition by birth cohort.4

4Other data exist to track the college major composition of degrees earned, using administrative counts from each

U.S. college and university, collected by the Higher Education General Information Survey (HEGIS) and Integrated

Postsecondary Education Data System (IPEDS) surveys since the mid-1960s (for graduates born approximately since

EVOLUTION OF COLLEGE MAJOR CHOICES 25

We combine the NSCG data with the CPS, Census, and other data sets and use the combined

data to provide a fuller picture of the trends in human capital investments and as the basis of our

estimation framework for the choice model. Identification of the time series for major-specific

skill rental rates is a key issue here, given that we do not observe the long-term major-specific

wage rates in the CPS or Census and have only a limited number of years of earnings by

major from the NSCG. We show how one can use cohort differences in average wages for

each calendar year (from the CPS and Census), combined with the proportion of each cohort

graduating with each major (from the NSCG), to identify major-specific skill rental rates. Unlike

previous studies that explicitly specify an aggregate production technology and use equilibrium

supply and demand conditions to identify skill rental rates (e.g., Lee, 2005; Lee and Wolpin,

2006, 2009), we sidestep the issue of specifying the technology by treating the skill prices as

unknown parameters and directly estimating the nonstationary sequence of prices along with

the other model parameters. This procedure avoids the considerable computational cost of

computing the equilibrium for each trial vector of parameters and allows us to more robustly

estimate other model parameters by avoiding misspecifying the technology.

We decompose the across cohort changes in educational attainment and major selection into

three channels: (i) changes in gender neutral relative major-specific skill rental rates, (ii) changes

in gender and major neutral post-secondary tuition rates, and (iii) changes in the gender-specific

value of home/leisure. We find that all three channels played a quantitatively important role in

determining male and female human capital investments.

Our estimates indicate that the rental rate of science and business major-specific skills in-

creased relative to humanities skills during the 1980s and 1990s, and this shift caused higher

college attendance and a shift toward science and business degrees for both men and women.

Both men and women responded to these changes in skill rental rates, but, because of their

lower level of home utility and higher expected future labor supply, men were more responsive

than women. An increase in the cost of tuition during this period discouraged college attendance

and partially offset the change in skill prices. The effect of higher schooling costs on college

major composition is theoretically ambiguous, but given the distribution of skills and tastes we

estimate, we find that higher tuition reduced the proportion of individuals who would have

otherwise completed science and business majors from entering college at all, which militated

against the changes in skill prices favoring these fields. An important factor in the increase in

female college graduation and the shift toward science and business fields was a reduction in the

value of time in the home for women and higher expected future labor supply. We do not model

the explicit mechanisms of the changes in home value, and the current literature offers several

possible candidate explanations, including changes in the price of home goods (Greenwood

et al., 2005), an increase in the availability of oral contraceptive (Goldin and Katz, 2002; Bailey,

2006), and changes in cultural norms with regard to women’s participation in the labor force

(Fernandez et al., 2004).5Our estimates are in line with these findings, and we show that these

types of mechanisms can also account for a shift in the college major composition for women.

Our research builds on previous studies that model college major choices. A number of papers

have examined field of study choices in equilibrium models, focusing on particular fields such

as engineers, lawyers, or teachers (Freeman, 1975a, 1975b, 1976; Siow, 1984; Zarkin, 1985).Our

framework generalizes these studies by jointly modeling the lifetime sequence of education and

labor supply choices, examining multiple fields instead of one field in isolation, and incorporating

heterogeneity in skills and tastes. Later work has studied field of choices using single cohort,

partial equilibriummodels, incorporating such factors as heterogeneity in earnings and tastes,

the mid-1940s). However, the NSCG data have several advantages: (i) they provide college major composition by birth

cohort instead of for graduating classes and therefore provide information on the life cycle timing of college decisions,

(ii) the data are available for a longer span of cohorts, allowing greater historical coverage, and (iii) the NSCG data

provide contemporaneous earnings and labor supply information linked to college major.

5See Goldin (2005) and Goldin et al. (2006b) for a summary of other possible factors behind the growth in women’s

labor force participation and educational attainment, including changes in divorce rates and workplace discrimination

laws. In addition, Charles and Luoh (2003) argue for the importance of earnings risk differences.