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High School Choices And The Gender Gap In Stem

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  • David Card
  • A. Abigail Payne

Abstract

Women are less likely than men to graduate with a degree in science, technology, engineering, or math (STEM). We use detailed administrative data for a recent cohort of Ontario high school students, combined with data from the province's university admission system, to analyze the sources of this gap. We show that entry to STEM programs is mediated through an index of STEM readiness that depends on end‐of‐high school courses in math and science. Most of the gender gap in STEM entry can be traced to differences in the share of college entrants who are STEM‐ready; only a small share is attributable to differences in the choice of major conditional on readiness. We then use high school course data to decompose the gender gap in STEM readiness into two channels: one reflecting the gap in the fraction of high school students with the necessary prerequisites to enter STEM, the other arising from differences in the overall fractions of females and males who enter university. The gender gap in the fraction of males and females with STEM prerequisites is small. The primary driver of the gap in STEM readiness is the low rate of university entry by nonscience‐oriented males. (JEL I21, 28, I20)

Suggested Citation

  • David Card & A. Abigail Payne, 2021. "High School Choices And The Gender Gap In Stem," Economic Inquiry, Western Economic Association International, vol. 59(1), pages 9-28, January.
    • Handle: RePEc:bla:ecinqu:v:59:y:2021:i:1:p:9-28
    DOI: 10.1111/ecin.12934
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    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, June.
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Morin, Louis-Philippe, 2015. "Cohort size and youth earnings: Evidence from a quasi-experiment," Labour Economics, Elsevier, vol. 32(C), pages 99-111.
    10. 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.
    11. Jacob, Brian A., 2002. "Where the boys aren't: non-cognitive skills, returns to school and the gender gap in higher education," Economics of Education Review, Elsevier, vol. 21(6), pages 589-598, December.
    12. Arcidiacono, Peter, 2004. "Ability sorting and the returns to college major," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 343-375.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
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    More about this item

    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination

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