The diffusion of AI-enabled technologies is transforming labour markets. This column examines the link between the diffusion of these technologies and changes in the female employment share in 16 European countries from 2011 to 2019. Average female employment shares increased over the period in occupations more exposed to AI. Countries with high initial female labour force participation and higher initial educational attainments of women relative to men show a stronger positive association.
Technological change transforms the range of activities that workers engage in and typically has distributional consequences. Skill-biased technological change during the 1970s and 1980s increased the demand for educated workers at the expense of those with lower levels of formal education (Autor et al. 1998, Autor and Katz 1999, and Acemoglu 2020), whereas automation technologies widely adopted starting in the 1990s reduced demand for routine jobs in the middle of the wage distribution (Autor et al. 2003 and Goos and Manning 2007).
The effect of these technologies also differs by gender. Mechanisation and skill-biased technological change favoured women due to their comparative advantage in intellectual activities compared to physical labour (Galor and Weil 2000). Though women were more exposed to the adverse effects of automation (Cortes and Pan 2019, Albanesi and Kim 2021), their educational advancements and interpersonal skills allowed them to gain in employment by shifting to professional occupations, whereas men shifted into lower-level service jobs (Cortes et al. 2024).
The most recent wave of innovation has been driven by the development of artificial intelligence (AI)-enabled technologies. These applications are based on algorithms that learn to perform tasks by following statistical patterns in data and generate a general-purpose technology that enables automation of non-routine and creative tasks, both in manufacturing and services.
In Albanesi et al. (2023), we examine the link between AI-enabled technologies and employment shares in occupation-sector cells in 16 European countries (Austria, Belgium, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Portugal, Spain, and the UK) over the period 2011–2019. We find that occupations potentially more exposed to AI-enabled technologies increased their employment share.
AI-enabled technologies on employment: Differential impact by gender
A natural follow-up question is whether the diffusion of these technologies will have differential impacts by gender. We address this question in a recent paper, Albanesi et al. (2025b), by examining the association between AI-enabled technologies and the female employment share in occupation-sector cells for the same set of countries over the same period, 2011–2019. We use data at the three-digit occupation level (according to the International Standard Classification of Occupations) from the Eurostat Labour Force Survey and measure exposure to AI at the occupation level with two existing measures developed for the US.
The first is the AI Occupational Impact score from Felten et al. (2019). This measure links advances in AI applications, such as finding patterns in data and making predictions about the future, to the abilities required by an occupation. The second measure, from Webb (2020), quantifies AI exposure based on the textual overlap of patents from Google Patents Public Data with task-based occupation descriptions, such as predicting prognosis and treatment, detecting cancer, identifying damage, and detecting fraud. These measures capture the extent to which occupations could be performed by AI and can therefore serve as proxies for potential AI-enabled automation.
With both measures, we find that higher exposure to AI is associated with an increase in the cells’ share of overall female employment. On average, moving up 10 centiles along the exposure distribution is associated with a 2.2%–2.9% increase in the share of female employment overall. These estimates are approximately double that for the total employment share in Albanesi et al. (2023, 2025a). Also, the statistically positive association between AI exposure and the female employment share is more robust across occupations than the association between the total employment share and exposure to AI, which was largely driven by professional occupations.
While there exists heterogeneity across countries, almost all show a positive relation between changes in female employment shares within occupations and exposure to AI-enabled automation.
Educational attainment as a factor
Educational attainment is an important factor for the impact of new technologies on employment, with highly educated workers most likely to reap any benefits in employment from the diffusion of new technologies (Albanesi et al. 2023, 2025a).
Given the large variation in women’s educational attainment in our sample, we stratify the results by countries’ average female educational attainment. We find a stronger association between exposure to AI-enabled technologies and the female share of employment in countries that have experienced greater increases in female educational attainment. In those countries, moving 10 centiles up along the distribution of exposure to AI is estimated to be associated with an increase of occupation-sector female employment share of 2.7% using Webb’s exposure measure, and of 3.4% using the measure from Felten et al. (Figure 1, panels a and b).
Figure 1 Exposure to AI and changes in female employment shares, by female participation and education
Notes: Regression coefficients measure the association between exposure to technology and changes in the female employment share. Each observation is an International Standard Classification of Occupations (ISCO) 3-digit occupation-sector cell. Observations are weighted by the average labour supply in the cell. Industry and country dummies included. Sample: 16 European countries, 2011–2019. The coefficient for the full sample is indicated by the horizontal dashed line. The bars in panels (a) and (b) show the coefficient estimated for the subsample of countries according to the average change between 2011 and 2019 in women’s educational attainment relative to that of men in the same country. The bars labelled ‘High Education’ show the results for the group of countries with a high relative increase in women’s educational attainment, while those labelled ‘Low Education’ show the results for the group of countries where the relative increase in women’s educational attainment is lower than the average of all countries in the sample. The bars in panels (c) and (d) show the estimated coefficient for the subsample according to the women’s participation rate in 2011. ‘Low’ or ‘High Participation’ countries are those where female participation in 2011 was lower/higher than the average for all countries in the sample.
Source: Albanesi et al. (2025).
Labour market attachment as a factor
Labour market attachment is also an important factor in the response to economic shocks, with higher participation associated with higher employment rates and lower unemployment rates for women (Albanesi and Sahin 2018). In our sample, countries with lower initial levels of female participation exhibit stronger positive trends in female employment growth. This underlying trend could affect female employment shares independently of AI exposure.
To account for this effect, we stratify our analysis based on women’s labour-force participation rates in 2011. Our findings indicate that the association between the female share of employment and AI exposure is stronger in countries with high initial levels of female participation for both measures of AI exposure (Figure 1, panels c] and d of measures for countries with higher relative female education).
This pattern suggests that greater attachment to the labour force enables women to minimise any displacement effects associated with the diffusion of these technologies, and the positive association between female employment share and AI exposure is not mechanically driven by faster growth in women’s employment in lower initial-participation countries.
AI-enabled technologies can boost women’s employment
To sum up, our results are consistent with the idea that the diffusion of AI-enabled technologies can benefit female employment, and that this benefit is amplified by higher levels of education.
Moreover, the positive association between female employment share and exposure to AI-enabled technologies is stronger in countries with higher initial female-labour-force participation suggests that greater labour force attachment and work experience enable women to minimise any displacement effects associated with the diffusion of these new technologies. These findings also support the notion in UNESCO (2022) that educational credentials are crucial for harnessing any beneficial impacts of AI for female employment.
Acemoglu et al. (2022) show that older workers are employed in occupations that differ from younger workers in many ways, and that AI seems to have the potential to create an ‘age-friendly work environment’. Similarly, our findings suggest that AI also has the potential to promote gender-friendly jobs.
Source : VOXeu
