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Academic Aptitude Signals and STEM field participation: A Regression Discontinuity Approach

Author

Listed:
  • Marcos Agurto

    (Universidad de Piura)

  • Sandra Buzinsky

    (Universidad de Piura)

  • Siddharth Hari

    (The World Bank)

  • Valeria Quevedo

    (Universidad de Piura)

  • Sudipta Sarangi

    (Virginia Tech)

  • Susana Vegas

    (Universidad de Piura)

Abstract
Gender disparities in STEM field participation at all levels are wide and persistent. In this paper we explore whether external signals about academic aptitude can influence female participation in STEM fields. We analyze 10 years of data on aptitude tests administered by a private university in Peru taken by 3,000 high school students each year. Prior to the test, students are asked to state their (non-binding) preferences over college majors. Admission into majors is determined on the basis of cut-off scores on the exam, which has a math and a verbal component. Using a regression discontinuity design, we find that among students whose preferred major was other than engineering, making the engineering cut-off increases the likelihood of enrolling in engineering by 10-12 percentage points. These effects are driven entirely by female students, and no effect is seen for males. We also find that women with higher scores on the verbal component are less likely to make this switch, reinforcing the idea that external signals about aptitude matter for choice of college majors. These results highlight the importance of external validation in influencing career choices in a context where social norms discourage female participation in STEM fields, and have important policy implications.

Suggested Citation

  • Marcos Agurto & Sandra Buzinsky & Siddharth Hari & Valeria Quevedo & Sudipta Sarangi & Susana Vegas, 2020. "Academic Aptitude Signals and STEM field participation: A Regression Discontinuity Approach," Working Papers 2020-08, Lima School of Economics.
    • Handle: RePEc:ima:wpaper:2020-008
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    STEM; Gender Gap; Academic Aptitude Signals; RD; Peru;
    All these keywords.

    JEL classification:

    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • H1 - Public Economics - - Structure and Scope of Government

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