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Does More Math in High School Increase the Share of Female STEM Workers? Evidence from a Curriculum Reform

Author

Listed:
  • Biewen, Martin

    () (University of Tuebingen)

  • Schwerter, Jakob

    () (University of Tübingen)

Abstract

This paper studies the consequences of a curriculum reform of the last two years of high school in one of the German federal states on the share of male and female students who complete degrees in STEM subjects and who later work in STEM occupations. The reform had two important aspects: (i) it equalized all students' exposure to math by making advanced math compulsory in the last two years of high school; and (ii) it roughly doubled the instruction time and increased the level of instruction in math and the natural sciences for some 80 percent of students, more so for females than for males. Our results provide some evidence that the reform had positive effects on the share of men completing STEM degrees and later working in STEM occupations but no such effects for women. The positive effects for men appear to be driven by a positive effect for engineering and computer science, which was partly counteracted by a negative effect for math and physics.

Suggested Citation

  • Biewen, Martin & Schwerter, Jakob, 2019. "Does More Math in High School Increase the Share of Female STEM Workers? Evidence from a Curriculum Reform," IZA Discussion Papers 12236, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp12236
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    1. Del Carpio, Lucía & Guadalupe, Maria, 2018. "More Women in Tech? Evidence from a Field Experiment Addressing Social Identity," IZA Discussion Papers 11876, Institute of Labor Economics (IZA).
    2. Berlingieri, Francesco & Zierahn, Ulrich, 2014. "Field of study, qualification mismatch, and wages: Does sorting matter?," ZEW Discussion Papers 14-076, ZEW - Leibniz Centre for European Economic Research.
    3. James G. MacKinnon & Matthew D. Webb, 2018. "The wild bootstrap for few (treated) clusters," Econometrics Journal, Royal Economic Society, vol. 21(2), pages 114-135, June.
    4. Levine, Phillip B & Zimmerman, David J, 1995. "The Benefit of Additional High-School Math and Science Classes for Young Men and Women," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(2), pages 137-149, April.
    5. Scott E. Carrell & Marianne E. Page & James E. West, 2010. "Sex and Science: How Professor Gender Perpetuates the Gender Gap," The Quarterly Journal of Economics, Oxford University Press, vol. 125(3), pages 1101-1144.
    6. Kalena E. Cortes & Joshua S. Goodman & Takako Nomi, 2015. "Intensive Math Instruction and Educational Attainment: Long-Run Impacts of Double-Dose Algebra," Journal of Human Resources, University of Wisconsin Press, vol. 50(1), pages 108-158.
    7. Joshua Goodman, 2017. "The Labor of Division: Returns to Compulsory High School Math Coursework," NBER Working Papers 23063, National Bureau of Economic Research, Inc.
    8. 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.
    9. Juanna Schrøter Joensen & Helena Skyt Nielsen, 2016. "Mathematics and Gender: Heterogeneity in Causes and Consequences," Economic Journal, Royal Economic Society, vol. 126(593), pages 1129-1163, June.
    10. Thomas Buser & Noemi Peter & Stefan C. Wolter, 2017. "Gender, Competitiveness, and Study Choices in High School: Evidence from Switzerland," American Economic Review, American Economic Association, vol. 107(5), pages 125-130, May.
    11. Joseph G. Altonji, 1995. "The Effects of High School Curriculum on Education and Labor Market Outcomes," Journal of Human Resources, University of Wisconsin Press, vol. 30(3), pages 409-438.
    12. James G. MacKinnon & Matthew D. Webb, 2017. "Pitfalls When Estimating Treatment Effects Using Clustered Data," Working Paper 1387, Economics Department, Queen's University.
    13. Görlitz, Katja & Gravert, Christina, 2015. "The Effects of Increasing the Standards of the High School Curriculum on School Dropout," IZA Discussion Papers 8766, Institute of Labor Economics (IZA).
    14. Katja Görlitz & Christina Gravert, 2018. "The effects of a high school curriculum reform on university enrollment and the choice of college major," Education Economics, Taylor & Francis Journals, vol. 26(3), pages 321-336, May.
    15. David Roodman & James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2019. "Fast and wild: Bootstrap inference in Stata using boottest," Stata Journal, StataCorp LP, vol. 19(1), pages 4-60, March.
    16. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    17. Matthew Wiswall & Basit Zafar, 2015. "Determinants of College Major Choice: Identification using an Information Experiment," Review of Economic Studies, Oxford University Press, vol. 82(2), pages 791-824.
    18. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, Oxford University Press, vol. 119(1), pages 249-275.
    19. Heather Rose & Julian R. Betts, 2004. "The Effect of High School Courses on Earnings," The Review of Economics and Statistics, MIT Press, vol. 86(2), pages 497-513, May.
    20. Kokkelenberg, Edward C. & Sinha, Esha, 2010. "Who succeeds in STEM studies? An analysis of Binghamton University undergraduate students," Economics of Education Review, Elsevier, vol. 29(6), pages 935-946, December.
    21. 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.
    22. Stijn Broecke, 2013. "Does offering more science at school increase the supply of scientists?," Education Economics, Taylor & Francis Journals, vol. 21(4), pages 325-342, September.
    23. repec:eee:ecoedu:v:64:y:2018:i:c:p:282-297 is not listed on IDEAS
    24. Basit Zafar, 2013. "College Major Choice and the Gender Gap," Journal of Human Resources, University of Wisconsin Press, vol. 48(3), pages 545-595.
    25. Uri Gneezy & Muriel Niederle & Aldo Rustichini, 2003. "Performance in Competitive Environments: Gender Differences," The Quarterly Journal of Economics, Oxford University Press, vol. 118(3), pages 1049-1074.
    26. Del Carpio, Lucia & Guadalupe, Maria, 2018. "More Women in Tech? Evidence from a field experiment addressing social identity," CEPR Discussion Papers 13234, C.E.P.R. Discussion Papers.
    27. Aughinbaugh, Alison, 2012. "The effects of high school math curriculum on college attendance: Evidence from the NLSY97," Economics of Education Review, Elsevier, vol. 31(6), pages 861-870.
    28. De Philippis, Marta, 2016. "STEM graduates and secondary school curriculum: does early exposure to science matter?," LSE Research Online Documents on Economics 67679, London School of Economics and Political Science, LSE Library.
    29. Francesconi, Marco & Parey, Matthias, 2018. "Early gender gaps among university graduates," European Economic Review, Elsevier, vol. 109(C), pages 63-82.
    30. James G. MacKinnon & Matthew D. Webb, 2018. "Wild Bootstrap Randomization Inference For Few Treated Clusters," Working Paper 1404, Economics Department, Queen's University.
    31. Friedman-Sokuler, Naomi & Justman, Moshe, 2016. "Gender streaming and prior achievement in high school science and mathematics," Economics of Education Review, Elsevier, vol. 53(C), pages 230-253.
    32. Thomas N. Daymonti & Paul J. Andrisani, 1984. "Job Preferences, College Major, and the Gender Gap in Earnings," Journal of Human Resources, University of Wisconsin Press, vol. 19(3), pages 408-428.
    33. repec:eee:ecoedu:v:64:y:2018:i:c:p:129-143 is not listed on IDEAS
    34. Ehrenberg, Ronald G., 2010. "Analyzing the factors that influence persistence rates in STEM field, majors: Introduction to the symposium," Economics of Education Review, Elsevier, vol. 29(6), pages 888-891, December.
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    More about this item

    Keywords

    academic degrees; occupational choice; gender differences;

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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