人工智能与欧洲女性就业

AI andwomen’semployment inEurope Stefania Albanesi, António Dias da Silva,Juan F. Jimeno,Ana Lamo,Alena Wabitsch Disclaimer:Thispaper should not be reported as representing the views of theEuropean Central Bank(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. Abstract We examine the link between the diffusion of artificial intelligence (AI) en-abled technologies and changes in the female employment share in 16 Europeancountries over the period 2011-2019.Using data for occupations at the 3-digitlevel, we find that on average female employment shares increased in occupationsmore exposed to AI. Countries with high initial female labor force participationand higher initial female relative education show a stronger positive association.While there exists heterogeneity across countries, almost all show a positive re-lation between changes in female employment shares within occupations andexposure to AI-enabled automation. Keywords: artificial intelligence, employment, gender, skills, occupationsJEL codes: J23, O33 Non-technical summary The evolution of digital technologies has been characterised by significant changes tothe skills required in the labour market. Over the last 50 years, two major shifts haveemerged: first, an increased demand for high-skilled workers, often at the expense ofworkers with lower levels of formal education; and second, a greater need for workersin non-routine cognitive tasks, frequently impacting middle-skilled workers in routinetasks. The changing demand for skills has had different implications for employment ofmen and women. Previously, mechanisation and the growing demand for high-skilledlabour tended to favour women, due to their comparative advantage in intellectualactivities compared to physical labour.Although women were more exposed to theadverse effects of automation, their educational advancements and interpersonal skillsallowed them to gain in employment by shifting to professional occupations, whereasmen shifted into lower-level service jobs. This paper explores the impact of artificial intelligence (AI) on women’s employ-ment in 16 European countries from 2011 to 2019. As AI technologies advance, theytransform the labour market with tasks being automated and with the emergence ofnew tasks, potentially affecting gender employment dynamics. This paper builds uponAlbanesi et al. (2025) which analyses the impact of AI on employment. It uses mea-sures developed by Felten et al. (2019) and Webb (2020) to assess AI exposure atthe occupation level.These measures consider the alignment of AI capabilities withoccupational tasks. We find a positive association between AI exposure and the increase in female em-ployment share in occupations. On average, a rise in exposure to AI by ten percentilesleads to a 2.2-2.9% increase in female employment share. These estimates are approx-imately double than for the total employment share, in Albanesi et al. (2025).Thepositive association is observed for almost all countries. Our results suggest that educational attainment and labour force participation arekey factors enabling women to benefit from the spread of AI technologies in Europe.Countries where women have made greater educational progress and participate more inthe labour market see stronger positive effects on female employment from AI adoption. These findings are in line with broader research on the role of education in adapting totechnological change and underscore the importance of policies that promote educationand labour force participation. 1Introduction Technological change transforms the range of activities that workers engage in andtypically has distributional consequences. Skill biased technological change during the1970s and 1980s increased the demand for educated workers at the expense of thosewith lower levels of formal education (Autor et al. (1998), Autor and Katz (1999) andAcemoglu (2020)), whereas automation technologies widely adopted starting in the1990s reduced demand for routine jobs in the middle of the wage distribution (Autoret al. (2003) and Goos and Manning (2007)).The effect of these technologies alsodiffer by gender. Mechanization and skill biased technological change favored womendue to their comparative advantage in intellectual activities compared to physical labor(Galor and Weil (2000)). Though women were more exposed to the adverse effects ofautomation (Cortes and Pan (2019) and Albanesi and Kim (2021)), their educationaladvancements and superior interpersonal skills allowed them to gain in employment byshifting 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 artifi-cial intelligence (AI) enabled technologies. These applications are based on algorithmsthat learn to perform tasks by following statistical patterns in data and generate ageneral-purpose technology tha