Recent advances in artificial intelligence and machine-learning technologies have fuelled fears of potential job losses among some workers. While the net impact of new technology on total jobs can be negative, positive or neutral, some workers may be more affected than others depending on how easily robots and algorithms can replace them, or how easily their skills complement the new technology. In the case of women and men, it is not clear who is likely to be most affected. While women are more likely to hold a university degree (typically associated with non-routine work that is more difficu
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Recent advances in artificial intelligence and machine-learning technologies have fuelled fears of potential job losses among some workers. While the net impact of new technology on total jobs can be negative, positive or neutral, some workers may be more affected than others depending on how easily robots and algorithms can replace them, or how easily their skills complement the new technology. In the case of women and men, it is not clear who is likely to be most affected. While women are more likely to hold a university degree (typically associated with non-routine work that is more difficult to automate), they are also less likely to specialize in technology (which may limit their work opportunities in an increasingly digital world), but more likely to work in certain occupations that may be susceptible to automation (e.g., retail sales or clerical work). The objective of this study is to estimate the automation risks faced by women and men based on an existing methodology applied to Canadian data (the Longitudinal and International Study of Adults, Wave 3). The approach also uses expert consultations in the automatability of occupations, taking into account a wide range of tasks typically associated with those occupations (thus allowing automation risks to vary within occupations).
The study finds that 44.4 per cent of women in the paid workforce faced a moderate to high risk of job transformation as a result of automation (50 per cent probability or above), compared with only 34.8 per cent of men. Overall, the gap remains about the same when comparing women and men with similar characteristics, such as age, education, industry and occupation. However, several characteristics are associated with greater automation risks faced by women relative to men, including being aged 55 or older, having no postsecondary qualifications or postsecondary qualifications other than a degree, having low levels of literacy or numeracy proficiency, being born in Canada, having a disability, being a part-time worker, not being in a union or covered by a collective bargaining agreement, and being employed in a small to mid-sized firm.
Published abstract.
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