Article 2020

Labor market returns to college major specificity

European Economic Review
Journal · Vol. 128 · Art. 103489
Abstract

This paper develops a new approach to measuring human capital specificity, in the context of college majors, and estimates its labor market return over a worker's life cycle. To measure specificity, we propose a novel method grounded in human capital theory: a Gini coefficient of earnings premia for a major across occupations. Our measure captures the notion of skill transferability across jobs. Education and nursing are the most specific majors, while philosophy and psychology are among the most general. Using data from the American Community Survey, we find that the most specific majors typically pay off the most, with an early-career earnings premium of about 5–6% over average majors (15-20% over the most general majors), driven by higher hourly wages. General majors lag far behind at every age. Despite their earnings advantage, graduates from specific majors are the least likely to hold managerial positions, with graduates from majors of average specificity being the most likely to do so. It may be that managerial positions require a mix of specific knowledge and broadly applicable skills. © 2020

Keywords

Author Keywords

J24 I23 I26 J31 JEL classification

Index Keywords

human capital United States returns to education labor market educational attainment labor standard income
Author Affiliations
University of St Andrews, St Andrews, Fife, United Kingdom
University of Memphis, Memphis, TN, United States
Funding & Acknowledgements
Acadia University
We thank Mark Borgschulte, David Bradford, Celeste Carruthers, Tugce Cuhadaroglu, Eric Eide, Bill Smith, two anonymous referees and an editor, seminar participants at the University of St Andrews, Acadia University, Mount Allison University, University of Essex, University of Illinois, the International Workshop on the Applied Economics of Education, & The IZA World Labor Conference for helpful comments. We gratefully acknowledge the National Center for Education Statistics for granting access to restricted longitudinal data.
University of St Andrews
We thank Mark Borgschulte, David Bradford, Celeste Carruthers, Tugce Cuhadaroglu, Eric Eide, Bill Smith, two anonymous referees and an editor, seminar participants at the University of St Andrews, Acadia University, Mount Allison University, University of Essex, University of Illinois, the International Workshop on the Applied Economics of Education, & The IZA World Labor Conference for helpful comments. We gratefully acknowledge the National Center for Education Statistics for granting access to restricted longitudinal data.
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7 undefined
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