The Contribution of College Majors to Gender and Racial Earnings Differences

 Gender and racial/ethnic gaps in labor market earnings remain large, even among college-goers. Cross-gender and race/ethnic differences in choice of and returns to college major are potentially important contributors. Following Texas public high school graduates for up to 20 years through college and the labor market, this study assesses gender and racial differences in college major choices and the consequences of these choices. 

Women and underrepresented minorities are less likely than men, Whites, and Asians to major in high earning fields like business, economics, engineering, and computer science, however we also show that they experience lower returns to these majors. 

Differences in major-specific returns relative to liberal arts explain about one quarter of the gender, White-Black, and White-Hispanic (but not White-Asian) earnings gaps among four-year college students and become larger contributors to earnings gaps than differential major distributions as workers age. 

The study presents suggestive evidence that differences in occupation choices within field are a key driver of the differences in returns across groups. The work shines light on the roles that college major choice and returns by gender and race contribute to inequality.

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College Major Choice, Payoffs, and Gender Gaps

This paper studies how college major choices shape earnings and fertility outcomes. Using administrative data that link students' preferences, random assignment to majors, and post-college outcomes, we estimate the causal pecuniary and non-pecuniary returns to different fields of study. 

The authors document substantial heterogeneity in these returns across majors and show that such variation helps explain gender gaps in labor market outcomes: women place greater weight on balancing career and family in their major choices, and these preference differences account for about 30% of the gender earnings gap among college graduates. 

The authors find that gender quotas in high-return fields can significantly reduce representation and earnings gaps with minimal impacts on efficiency and aggregate fertility.