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Women across Europe are substantially underrepresented in advanced digital tasks — about a 15 percentage-point average gap that becomes a sharp 'digital glass ceiling' at the highest digital-intensity jobs. Observable characteristics explain only roughly 30% of the gap, implying workplace organisation, task allocation and promotion practices — not just STEM supply — must be addressed (Ireland shows an especially large male concentration in advanced digital roles).

Squandered skills? Bridging the digital gender skills gap for inclusive growth in Ireland – A comparative European perspective

Adele Whelan, Luke Brosnan, S. McGuinness · Fetched March 15, 2026

semantic_scholar correlational medium evidence 7/10 relevance DOI Source

Using ESJS 2021, the paper documents that women across Europe are about 15 percentage points less likely than men to perform advanced digital tasks, with observable factors explaining only ~30% of the gap and the disparity concentrated at the top of the job digital-intensity distribution and among younger cohorts.

Rapid digital transformation, reflected in the growing use of digital technologies across jobs, is reshaping work in Ireland and Europe. This makes it essential to understand digital skill use in order to ensure inclusive economic growth, where the benefits of technological change are widely shared. Persistent gender gaps in access to advanced digital tasks matter because exposure to these tasks is often a stepping stone to higher-quality jobs, leadership pathways, and more productivity-enhancing work, so such disparities can reinforce wider labour market inequalities. This report examines the gender gaps in workplace digital task use, with a specific focus on Ireland, using the European Skills and Jobs Survey (ESJS) (Cedefop, 2021). We distinguish between basic digital tasks such as routine use of internet, word processing and spreadsheets, and advanced digital tasks including programming, AI/machine learning, and IT system management. We also construct a Job Digital Intensity Index (JDII) , which captures how digitally intensive jobs are overall, based on the range of digital tasks performed. Our analysis combines regression-based estimates, decomposition techniques, and distributional analysis to examine gender differences in digital task use and digital job intensity. Across Europe, women are around 15 percentage points less likely than men to perform advanced digital tasks in their jobs. Differences in observable worker and job characteristics, such as education, field of study, occupation and sector, explain only a minority of this gap, accounting for around 30 per cent on average. The remaining difference is not explained by the factors observed in the data, indicating that additional influences (not captured in the survey) may also play an important role. We find that gender disparities widen significantly at the very upper end of the distribution. While the lower and middle levels of digital intensity show more modest differences, the gap becomes most pronounced for jobs requiring the most digitally intensive range of tasks, pointing to a ‘digital glass ceiling’ within workplaces. Across Europe, the analysis also shows that gender gaps are larger and less well explained by observable characteristics among younger cohorts (aged under 35). This suggests that the under-representation of women in advanced digital roles is not a legacy issue confined to older cohorts, but one that continues to emerge early in careers. Ireland stands out in the European context. It exhibits the largest gender gap in advanced digital task use, with approximately 44 per cent of men versus 18 per cent of women performing advanced digital tasks, a difference of 26 percentage points, close to double the European average. Importantly, women in Ireland use advanced digital skills at rates broadly comparable to women elsewhere in Europe. Ireland’s large gap instead reflects particularly high rates of advanced digital task use among men. While differences in the types of jobs men and women do (often referred to as occupational sorting) explains a somewhat larger share of the gap in Ireland than in other European countries, a substantial portion remains unexplained, highlighting the potential influence of unobserved structural, cultural or other organisational factors specific to the Irish labour market. Overall, the evidence shows that closing the gender gap in digital skill use at work will require more than increasing women’s participation in science, technology, engineering and maths (STEM) education or occupations. While education and access to digital jobs are important, the results highlight the need for further research into other factors that may shape opportunities to develop and apply advanced digital skills, including workplace organisation, task allocation, progression pathways, and broader organisational practices. Addressing these issues will be important not only for gender equality, but also for productivity, innovation and inclusive economic growth in Ireland.

Summary

Main Finding

Across Europe, women are substantially less likely than men to perform advanced digital tasks at work, and observable characteristics (education, occupation, sector, etc.) explain only about 30% of this gap. The disparity is most pronounced at the top of the digital-intensity distribution (a “digital glass ceiling”) and is larger among younger cohorts. Ireland shows an especially large gap driven largely by very high rates of advanced digital task use among men (44% men vs 18% women; 26 percentage points), while Irish women’s rates are similar to their European peers—implying country-specific factors boost men’s exposure to advanced digital tasks.

Key Points

Definitions
- Basic digital tasks: routine internet use, word processing, spreadsheets.
- Advanced digital tasks: programming, AI/machine learning, IT system management.
- Job Digital Intensity Index (JDII): an index capturing how digitally intensive jobs are based on the range of digital tasks performed.
Europe-wide results
- Women are ~15 percentage points less likely than men to perform advanced digital tasks.
- Observable worker and job characteristics explain ~30% of the European gap on average; ~70% remains unexplained by the survey variables.
- The gender gap expands sharply at the highest levels of digital intensity (digital glass ceiling).
- Gaps are larger and less well explained among workers under 35, indicating the problem emerges early in careers.
Ireland-specific results
- Large gender gap in advanced digital task use: 44% of men vs 18% of women (26 pp).
- Irish women’s advanced digital task use is broadly comparable to women elsewhere; the gap reflects especially high male participation in advanced tasks.
- Occupational sorting explains a somewhat larger share of the Irish gap than in other countries, but a substantial portion remains unexplained.
Policy-relevant insight: Closing the gap requires more than boosting STEM participation; workplace organisation, task allocation, progression pathways and organisational practices likely matter.

Data & Methods

Data source: European Skills and Jobs Survey (ESJS) (Cedefop, 2021).
Measures constructed:
- Binary indicators for basic and advanced digital task use.
- Job Digital Intensity Index (JDII) summarising overall digital task intensity.
Empirical approaches:
- Regression analysis to estimate raw gender differences controlling for observable characteristics.
- Decomposition techniques to quantify how much of the gender gaps are explained by observed factors (education, field of study, occupation, sector, age/cohort, etc.).
- Distributional analysis to assess how gaps vary across the JDII distribution (identifying concentration at the top).
Robustness: Cohort analysis (younger vs older workers) and cross-country comparisons highlighting Ireland as an outlier in male digital task intensity.

Implications for AI Economics

Labor-market impacts
- Unequal exposure to advanced digital/AI tasks can reinforce broader gender inequalities in wages, promotion, and access to high-productivity work.
- A “digital glass ceiling” reduces female representation in the roles that drive AI development, deployment, and high-value complementarities.
Policy and organisational priorities beyond education
- Interventions should target workplace practices: task allocation, promotion criteria, mentoring/progression pathways, and job design to ensure equitable access to advanced tasks.
- Monitoring and measurement: use JDII-like metrics in firm- and sector-level diagnostics to track gendered task exposure and evaluate interventions.
Implications for AI adoption and productivity
- Widespread and equitable diffusion of AI complements productivity only if diverse workforces can access and apply advanced digital skills; exclusion of women risks underutilising human capital and slowing inclusive productivity gains.
- Gendered patterns in task allocation may bias which AI tools are developed, adopted, or trained—affecting fairness and applicability of AI systems.
Research priorities for AI economics
- Investigate the unexplained portion: firm-level organisation, managerial practices, informal networks, discrimination, task-level barriers, and cultural norms.
- Longitudinal and experimental studies to identify causal pathways from education, hiring, task assignment, and promotion to advanced digital task exposure.
- Analyse how AI adoption interacts with gendered task allocation — does AI automate tasks women disproportionately perform or open new advanced-skill opportunities, and for whom?
Policy levers
- Combine supply-side (training, reskilling) with demand-side (transparent task allocation, promotion audits, mentoring, targeted on-the-job opportunities) measures.
- Encourage firms to track gender composition across JDII deciles and set targets for equitable access to advanced tasks.

Assessment

Paper Typecorrelational Evidence Strengthmedium — Uses a large, recent, cross-sectional, pan-European survey (ESJS 2021) with detailed task measures and standard regression/decomposition tools to document robust associations and an unexplained residual; however, the design is observational and cross-sectional, so causal mechanisms cannot be established and results may be affected by measurement error and omitted variables (notably firm-level practices and unobserved selection). Methods Rigormedium — Appropriate and transparent descriptive statistics, regression controls, decomposition techniques, cohort analysis and cross-country comparisons are used to probe robustness and heterogeneity; but there is no causal identification strategy (e.g., experiments, natural experiments, IVs, or longitudinal causal methods), and key potential confounders (firm-level organization, managerial allocation, informal networks) are not observed. SampleEuropean Skills and Jobs Survey (ESJS) 2021 — cross-sectional, worker-level survey covering employed adults across European countries with self-reported measures of basic and advanced digital tasks, education, field of study, occupation, sector, age/cohort and other demographics; sample sizes vary by country and Ireland is highlighted as an outlier. Themesinequality labor_markets skills_training org_design GeneralizabilityLimited to surveyed European countries and the ESJS sampling frame (may not apply outside Europe), Cross-sectional design prevents inference about dynamics or causal pathways over time, Relies on self-reported task measures which may vary in interpretation across respondents/countries, Omits firm-level variables and managerial practices that likely drive task assignment, Findings apply to employed workers and may not generalize to unemployed or informal sector populations, Country heterogeneity (labor market institutions, sectoral composition) limits simple cross-country extrapolation

Claims (10)

Claim	Direction	Confidence	Outcome	Details
Across Europe, women are around 15 percentage points less likely than men to perform advanced digital tasks in their jobs. Skill Acquisition	negative	high	Probability / share of workers performing advanced digital tasks (binary indicator of advanced digital task use)	Women ~15 percentage points less likely than men to perform advanced digital tasks across Europe 0.3
Differences in observable worker and job characteristics (education, field of study, occupation, sector) explain only a minority of the Europe-wide gender gap in advanced digital task use, accounting for around 30% on average. Skill Acquisition	mixed	medium-high	Proportion (%) of the gender gap in advanced digital task use explained by observable characteristics	Observable characteristics explain around 30% of the gender gap in advanced digital task use 0.03
The remaining difference (roughly 70%) is not explained by the factors observed in the data, indicating additional influences not captured in the survey. Skill Acquisition	negative	medium-high	Unexplained share (%) of the gender gap in advanced digital task use	roughly 70% unexplained 0.03
Gender disparities widen significantly at the very upper end of the distribution of digital job intensity — a 'digital glass ceiling' — while lower and middle levels show more modest differences. Skill Acquisition	negative	medium	Gender gap in Job Digital Intensity Index (JDII) at the upper tail (highly digitally intensive jobs)	larger gap at upper tail of JDII ("digital glass ceiling") 0.18
Gender gaps are larger and less well explained by observable characteristics among younger cohorts (aged under 35), implying under-representation of women in advanced digital roles is emerging early in careers. Skill Acquisition	negative	medium	Gender gap in advanced digital task use (and share explained by observables) for workers aged <35	gaps larger and less well explained among workers aged <35 0.18
Ireland exhibits the largest gender gap in advanced digital task use: approximately 44% of men versus 18% of women perform advanced digital tasks — a 26 percentage point gap, close to double the European average. Skill Acquisition	negative	high	Share (%) of men and women in Ireland performing advanced digital tasks; gender gap in percentage points	26 percentage point gender gap (44% men vs 18% women in Ireland) 0.3
Women in Ireland use advanced digital skills at rates broadly comparable to women elsewhere in Europe; Ireland's large gender gap instead reflects particularly high rates of advanced digital task use among men. Skill Acquisition	mixed	medium-high	Share (%) of women performing advanced digital tasks in Ireland versus the European average; share (%) of men performing advanced digital tasks in Ireland	Irish female rates comparable to European average; Irish male rates unusually high 0.03
Occupational sorting explains a somewhat larger share of the gender gap in Ireland than in other European countries, but a substantial portion remains unexplained, pointing to possible unobserved structural, cultural or organisational factors specific to the Irish labour market. Skill Acquisition	negative	medium	Portion (%) of Ireland's gender gap in advanced digital task use explained by occupation (and other observables) versus unexplained residual	occupational sorting explains a larger share in Ireland but substantial residual remains 0.18
A Job Digital Intensity Index (JDII) was constructed to capture how digitally intensive jobs are overall, based on the range of digital tasks performed. Other	null_result	high	Job Digital Intensity Index (JDII) — composite measure of digital task breadth/intensity	JDII constructed as composite measure of digital task breadth/intensity 0.3
Closing the gender gap in digital skill use at work will require more than increasing women’s participation in STEM education or occupations; workplace organisation, task allocation, progression pathways, and organisational practices also need attention. Governance And Regulation	positive	medium	Gender gap in digital skill use at work (target for policy action)	closing gap requires workplace organisation, task allocation, progression pathways, and organisational practice changes beyond STEM participation 0.18