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AI and Digital Technology: Gender Gaps in Higher Education

Author

Listed:
  • José Ignacio Conde-Ruiz
  • Juan José Ganuza Fernandez
  • Manuel García
  • Carlos Victoria Lanzón

Abstract

This article examines gender gaps in higher education in Spain in the context of technological advancements, particularly digitalization and artificial intelligence (AI). First, analyzing descriptive Spanish data we identify significant disparities, with women overrepresented in health-related fields and underrepresented in STEM (Science, Technology, Engineering and Mathematics) disciplines. This imbalance is concerning as STEM fields offer better employment prospects and higher salaries. Then, we analyze university degrees’ exposure to technological change through routine task intensity (RTI) and AI exposure indices, as well as a novel index of exposure to emerging digital technologies from 2015 to 2024. Our findings show that women are more enrolled in degrees with higher RTI, prone to automation, and less in degrees with higher AI exposure, likely to benefit from technological advancements. This suggests technological change could widen existing labor market gender gaps. To address this, we recommend policies to boost female participation in STEM fields and adapt educational curricula to reduce routine tasks and enhance AI complementarities, ensuring equitable labor market outcomes amid technological change. (JEL codes: I23, I26, J16, J24).

Suggested Citation

  • José Ignacio Conde-Ruiz & Juan José Ganuza Fernandez & Manuel García & Carlos Victoria Lanzón, 2024. "AI and Digital Technology: Gender Gaps in Higher Education," CESifo Economic Studies, CESifo Group, vol. 70(3), pages 244-270.
    • Handle: RePEc:oup:cesifo:v:70:y:2024:i:3:p:244-270.
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    File URL: http://hdl.handle.net/10.1093/cesifo/ifae020
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    References listed on IDEAS

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    1. Daron Acemoglu, 2024. "The Simple Macroeconomics of AI," NBER Working Papers 32487, National Bureau of Economic Research, Inc.
    2. Daron Acemoglu & Pascual Restrepo, 2020. "The wrong kind of AI? Artificial intelligence and the future of labour demand," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 13(1), pages 25-35.
    3. Hanushek, Eric A. & Schwerdt, Guido & Wiederhold, Simon & Woessmann, Ludger, 2015. "Returns to skills around the world: Evidence from PIAAC," European Economic Review, Elsevier, vol. 73(C), pages 103-130.
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    Cited by:

    1. José Ignacio Conde-Ruiz & Juan José Ganuza & Manu García & Carlos Victoria, 2024. "La Demanda de Educación Superior ante el Cambio Tecnológico y la Inteligencia Artificial," Studies on the Spanish Economy eee2024-09, FEDEA.

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    More about this item

    Keywords

    gender gaps; artificial intelligence; higher education; STEM; technological change; self-actualization;
    All these keywords.

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • I26 - Health, Education, and Welfare - - Education - - - Returns to Education
    • 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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