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High School Choices and the Gender Gap in STEM

Author

Listed:
  • David Card

    (Department of Economics, University of California, Berkeley; and National Bureau of Economic Research)

  • A. Abigail Payne

    (Melbourne Institute: Applied Economic & Social Research and Department of Economics, The University of Melbourne; and Department of Economics, McMaster University)

Abstract

Women who graduate from university are less likely than men to specialize in science, technology, engineering, or math (STEM). We use detailed administrative data for a recent cohort of high school students in Ontario, Canada, combined with data from the province's university admission system to analyze the dynamic process leading to this gap. We show that entry to STEM programs is mediated through an index of STEM readiness based on end-ofhigh-school courses in math and science. Most of the gender gap in STEM entry can be traced to differences in the rate of STEM readiness; less than a fifth is due to differences in the choice of major conditional on readiness. We then use high school course data to decompose the gap in STEM readiness among university entrants into two channels: one reflecting the gender gap in the fraction of high school students with the necessary prerequisites to enter STEM, and a second arising from differences in the fractions of females and males who enter university. The gender gap in the fraction of students with STEM prerequisites is small. The main factor is the lower university entry rate by men -- a difference that is due to the lower fraction of non-science oriented males who complete enough advanced level courses to qualify for university entry. We conclude that differences in course-taking patterns and preferences for STEM conditional on readiness contribute to male-female differences in the rate of entering STEM, but that the main source of the gap is the lower overall rate of university attendance by men.

Suggested Citation

  • David Card & A. Abigail Payne, 2017. "High School Choices and the Gender Gap in STEM," Melbourne Institute Working Paper Series wp2017n25, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    • Handle: RePEc:iae:iaewps:wp2017n25
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    References listed on IDEAS

    as
    1. Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), 2011. "Handbook of the Economics of Education," Handbook of the Economics of Education, Elsevier, edition 1, volume 4, number 4.
    2. Anne Ardila Brenøe & Ulf Zölitz, 2020. "Exposure to More Female Peers Widens the Gender Gap in STEM Participation," Journal of Labor Economics, University of Chicago Press, vol. 38(4), pages 1009-1054.
    3. Speer, Jamin D., 2017. "The gender gap in college major: Revisiting the role of pre-college factors," Labour Economics, Elsevier, vol. 44(C), pages 69-88.
    4. Brian A. Jacob, 2002. "Where the boys aren't: Non-cognitive skills, returns to school and the gender gap in higher education," NBER Working Papers 8964, National Bureau of Economic Research, Inc.
    5. Arcidiacono, Peter, 2004. "Ability sorting and the returns to college major," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 343-375.
    6. Joseph G. Altonji & Erica Blom & Costas Meghir, 2012. "Heterogeneity in Human Capital Investments: High School Curriculum, College Major, and Careers," Annual Review of Economics, Annual Reviews, vol. 4(1), pages 185-223, July.
    7. Morin, Louis-Philippe, 2015. "Cohort size and youth earnings: Evidence from a quasi-experiment," Labour Economics, Elsevier, vol. 32(C), pages 99-111.
    8. Betts, Julian R., 2011. "The Economics of Tracking in Education," Handbook of the Economics of Education, in: Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), Handbook of the Economics of Education, edition 1, volume 3, chapter 7, pages 341-381, Elsevier.
    9. Claudia Goldin & Lawrence F. Katz & Ilyana Kuziemko, 2006. "The Homecoming of American College Women: The Reversal of the College Gender Gap," Journal of Economic Perspectives, American Economic Association, vol. 20(4), pages 133-156, Fall.
    10. Wiswall, Matthew & Stiefel, Leanna & Schwartz, Amy Ellen & Boccardo, Jessica, 2014. "Does attending a STEM high school improve student performance? Evidence from New York City," Economics of Education Review, Elsevier, vol. 40(C), pages 93-105.
    11. Richard Sabot & John Wakeman-Linn, 1991. "Grade Inflation and Course Choice," Journal of Economic Perspectives, American Economic Association, vol. 5(1), pages 159-170, Winter.
    12. Altonji, J.G. & Arcidiacono, P. & Maurel, A., 2016. "The Analysis of Field Choice in College and Graduate School," Handbook of the Economics of Education,, Elsevier.
    13. Jessica Ellis & Bailey K Fosdick & Chris Rasmussen, 2016. "Women 1.5 Times More Likely to Leave STEM Pipeline after Calculus Compared to Men: Lack of Mathematical Confidence a Potential Culprit," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-14, July.
    14. Fischer, Stefanie, 2017. "The downside of good peers: How classroom composition differentially affects men's and women's STEM persistence," Labour Economics, Elsevier, vol. 46(C), pages 211-226.
    15. Steven Dieterle & Cassandra M. Guarino & Mark D. Reckase & Jeffrey M. Wooldridge, 2015. "How do Principals Assign Students to Teachers? Finding Evidence in Administrative Data and the Implications for Value Added," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 34(1), pages 32-58, January.
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    More about this item

    Keywords

    Post-secondary education; gender; STEM;
    All these keywords.

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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