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AI and Digital Technology: Gender Gaps in Higher Education

J. Ignacio Conde‐Ruiz et al.

CESifo Economic Studies2024https://doi.org/10.1093/cesifo/ifae020article
AJG 2ABDC C
Weight
0.73

What the paper says

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).

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https://doi.org/https://doi.org/10.1093/cesifo/ifae020

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@article{j.2024,
  title        = {{AI and Digital Technology: Gender Gaps in Higher Education}},
  author       = {J. Ignacio Conde‐Ruiz et al.},
  journal      = {CESifo Economic Studies},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1093/cesifo/ifae020},
}

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Evidence weight

0.73

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact1.00 × 0.4 = 0.40
M · momentum0.70 × 0.15 = 0.10
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.